博文

This paper explores the emerging research field of Semantic Mathematics and combines it with the DIKWP model in order to reveal the deeper connections between data, information, knowledge, wisdom, and purpose in the process of computation and reasoning. The core concept of Semantic Mathematics lies in the transformation from conceptual space to semantic space through new perspectives on conceptual relationships and the reconstruction of logical structures, so as to semantically process data, information, and knowledge, and to clarify and realize purpose.

The paper elaborates in detail how semantic mathematics is applied in the DIKWP model in various aspects such as semantic reconstruction of data, deep processing of information, construction of knowledge, and comprehensive application of wisdom, with particular emphasis on the key role of clarification of purpose. Meanwhile, semantic mathematics is also used to achieve the transformation, compensation and calibration of DIKWP resources, as well as the conversion process of subjective objectification, showing strong theoretical guidance and practical application value. The paper discusses in depth the specific roles of Essence Computation and Reasoning, Existence Computation and Reasoning, and Purpose Computation and Reasoning in the DIKWP model, and demonstrates their application effects in different domains, such as scientific research, medical diagnosis, and project management, etc., through example analysis.

Finally, the article further extends semantic mathematics to multiple levels of science and technology, including the essential semantics of integers, new interpretations of mathematical operations, new frameworks of mathematical logic and reasoning, and the deep integration of mathematics and philosophy, and shows the unique advantages and broad prospects of the new semantic mathematics in solving problems such as Goldbach's Conjecture and the four-color Theorem through a series of case studies. At the same time, the article also explores the application possibilities of semantic mathematics in the perspective of digital economy development of Hainan Free Trade Port and interpretation of classical culture, which fully reflects its importance and universality as a new type of mathematical tool in the information age.

1 Introduction

With the rapid development of science and technology and the advent of the information age, mathematics, as a basic science, plays a crucial role in promoting the development of various fields in modern society. However, traditional mathematical models gradually show their limitations in dealing with complex problems, and more in-depth semantic understanding and mathematical modelling methods are needed to adapt to the needs of the new era. The purpose of this thesis is to explore and introduce a new mathematical theoretical framework, Semantic Mathematics, and its application to the DIKWP models ( Essence Computation and Reasoning, Existence Computation and Reasoning, and Intention Computation and Reasoning).

Chapter 2 details the core ideas of semantic mathematics, a new perspective on conceptual relations, the reconstruction of logical structures, and the unification of abstraction and application. We emphasise the integration and validation of semantic mathematics and its value in interdisciplinary research. At the same time, we introduce the transformational framework of semantic mathematics and the semanticisation of data, information, knowledge, wisdom and purpose at the level of data, information, knowledge, wisdom and purpose, laying the groundwork for subsequent applications of the DIKWP model.

In Chapter 3, we introduce the DIKWP model of conceptual-semantic linkage and describe in detail its application at the level of data, information, knowledge, wisdom and purpose. We highlight the key role of semantic mathematics in the DIKWP model, including the perspectives of semantic-mathematical parsing of data, semantic-mathematical processing of information, complete semantic analysis of knowledge, semantic application of wisdom, and dichotomous group analysis of purpose. This chapter will provide the reader with a clear framework of the DIKWP model and presents the value of its practical applications at various levels.

In Chapter 4, we explore the application of semantic mathematics to DIKWP resource transformation, compensation and calibration, including the perspectives of semantic transformation of resources, semantic fusion processing between resources, and mutual semantic calibration. We present a future perspective, demonstrating the potential role of semantic mathematics in enabling subjective objectification.

Chapter 5 looks at the core elements of the DIKWP model, including Essence Computation and Reasoning, Existence Computation and Reasoning, and Purpose Computation and Reasoning. We explore in detail the application of semantic mathematics in these three perspectives, showing through concrete examples how semantic mathematics can empower the DIKWP model to achieve deep understanding and solution of complex problems.

Finally, we demonstrate the wide range of applications and far-reaching impact of new semantic mathematics in science and technology by exploring the deeper semantics of mathematics. We focus on the role of integer semantics in mathematical reasoning and look at the perspectives of extensions and applications of semantic mathematics to prime numbers, mathematical operations, mathematical logic and reasoning, and philosophy.

The aim of this paper is to provide readers with a comprehensive understanding of the application of semantic mathematics to the DIKWP model and to look forward to its far-reaching implications in science, technology, philosophy, and other fields. Through this paper, we expect to stimulate more research and discussion on semantic mathematics and the DIKWP model, and to promote innovation and development in the field of mathematics.

2 Semantic Mathematics and DIKWP Model (Essence Computation, Existence Computation and Purpose Computation)

2.1 Reinventing Mathematics - Semantic Mathematics

Semantic mathematics is a method to redefine the basic elements and concepts of mathematics. This new mathematical paradigm aims at a deeper understanding of mathematics and its application in modern science and technology.

2.1.1 Core concept of semantic mathematics

Semantic mathematics focuses on giving mathematical objects and operations deeper semantic meanings. It not only reinterprets numbers and operators, but also redefines mathematical logic and reasoning rules.

A new definition of basic elements: integers are not only the basic units of mathematics, but also carry specific semantics. For example, even numbers represent "identity" and prime numbers symbolize "pure semantics".

New interpretation of operators: traditional operators such as addition and multiplication have been given new meanings, for example, addition means "aggregation" between elements, while multiplication means "combination" or "fusion".

2.1.2 A new perspective of conceptual relationship

Semantic mathematics explores the new relationship between mathematical concepts and redefines the relationship between integer and fraction, addition and multiplication.

The relationship between integer and fraction: this not only represents the difference in quantity, but also symbolizes the semantics of integrity and segmentation.

A new understanding of addition and multiplication: addition becomes "aggregation" between elements, while multiplication evolves into "expansion" or "extension".

2.1.3 Reconstruction of logical structure

Under the new semantic framework, mathematical logic has undergone a profound reconstruction, emphasizing starting from known facts or assumptions and forming conclusions about unknown facts.

The new framework of mathematical logic: the process of proof and reasoning is no longer limited to symbolic operation, but is transformed into the process of semantic relationship and logical reasoning.

A new method of logical reasoning: emphasizing the conclusion of unknown facts from known facts through logical reasoning.

2.1.4 Abstraction and application

In the new framework, we discuss the exploration of advanced abstract concepts and their application in solving specific problems.

Exploration of advanced abstract concepts: set theory and function theory pay more attention to their semantic attributes in the new framework.

Application in practical problems: for example, in computer programming or data analysis.

2.1.5 Integration and verification

Integrate new concepts, relationships and logic into a coherent system, and verify its effectiveness through mathematical proof and practical problem solving.

2.1.6 Interdisciplinary research

Explore the intersection of new semantic mathematics with physics, computer science, philosophy and other disciplines, and explore new application fields such as artificial intelligence and big data analysis.

Semantic mathematics represents a great innovation in traditional mathematics, which not only redefines basic elements and concepts, but also provides new methods and tools for solving complex problems. This interdisciplinary integration indicates its wide application and important role in future scientific exploration and technological innovation. Through this research, we have a deeper understanding of semantic mathematics and see its potential and application prospect in modern science and technology.

2.2 Semantic Mathematics: the transformation from conceptual space to semantic space

The core of semantic mathematics is to transform DIKWP model from traditional concept space to richer semantic space, so as to realize objectification and accuracy of understanding and communication.

2.2.1 Transformation framework of Semantic Mathematics

Transformation from concept to semantics: traditional concepts are transformed into semantic entities with clear meaning and connotation, which makes the original vague concepts clearly defined and defined.

Deepening of understanding: through the method of semantic mathematics, understanding is no longer limited to the surface level, but can go deep into the core attributes and essence characteristics of concepts.

2.2.2 Semanticization of data and information

Semanticization of data: data is not only a collection of original facts, but also an information unit with specific semantics. Semantic mathematics helps us understand the deep meaning behind the data.

In-depth interpretation of information: the processing of information goes beyond simple data combination and turns into the interpretation and understanding of complex semantic relations between data.

2.2.3 Semantic reconstruction of knowledge and wisdom

Complete semantics of knowledge: knowledge is no longer an isolated piece of information, but a complete semantic network connecting different data and information.

Semantic application of wisdom: decision-making and thinking at the level of wisdom are reconstructed into semantic judgments based on deep values and ethical principles.

2.2.4 Clarification and realization of purpose

Semantic clarification of purpose: through semantic mathematics, purpose is transformed from vague goal to semantic entity with clear input and output.

Semantic path to achieve the goal: determine the specific semantic path to achieve the goal and improve the effectiveness and pertinence of the decision.

Semantic mathematics provides a new perspective for the processing of DIKWP model. The transformation from conceptual space to semantic space not only increases the depth and breadth of understanding, but also improves the accuracy and efficiency of communication. This transformation is of great significance to modern scientific research and technological innovation, and provides new tools and methods for dealing with complex problems. Through semantic mathematics, we can process data, refine information, build knowledge, apply wisdom and realize purposes more effectively.

2.3 Concept-semantic linkage DIKWP model: the application of semantic mathematics

Under the framework of semantic mathematics, DIKWP model realizes the deep transformation from concept to semantics, and the whole process from data to purpose is redefined and processed to ensure accuracy and consistency.

2.3.1 Semantic reconstruction of data

Data is no longer a simple collection of original facts, but is regarded as an entity with specific semantics. Semantic mathematics enables data to be interpreted and transformed into information with deep meaning, which lays the foundation for subsequent processing.

2.3.2 Deep processing of information

With the application of semantic mathematics, information is further processed and refined and transformed into knowledge. This involves extracting complete semantics from data and constructing a knowledge system.

2.3.3 Construction of knowledge

The construction of knowledge is no longer a simple information accumulation, but a deep semantic analysis based on data and information. Through abstraction and integration, a structured and internally related knowledge network is formed.

2.3.4 Comprehensive application of wisdom

Wisdom is interpreted as the comprehensive application and judgment of data, information and knowledge in the framework of semantic mathematics, and pays attention to the deep understanding of values and ethical principles. This promotes a more informed and comprehensive decision-making process.

2.3.5 Clarity of purpose

purpose is defined as an action plan with clear goals and paths. Semantic mathematics ensures a clear definition of purpose, thus making actions more purposeful and efficient.

2.3.6 Overall effect

The application of semantic mathematics not only enhances the depth of understanding at all levels of DIKWP model, but also strengthens the connection between concepts and semantics. This promotes more effective knowledge exchange and application.

2.3.7 Application value

This model has important application value in modern scientific research and technological development, and provides a new perspective and tool for dealing with complex problems.

Through the application of semantic mathematics, every link of DIKWP model has realized the deep transformation from concept to semantics, which makes the whole process not only more efficient, but also more purposeful and accurate. This method provides a brand-new framework and methodology for modern scientific research and technological innovation.

2.4 Application of semantic mathematics in DIKWP model

The application of semantic mathematics in DIKWP model (data, information, knowledge, wisdom and purpose) has great potential. Semantic mathematics provides us with a new way to understand and deal with these concepts.

2.4.1 Semantic Mathematical analysis of data

In semantic mathematics, data is regarded as the concrete manifestation of the same semantics in cognition. For example, for the concept of "sheep", although each sheep is different in shape and color, we can classify them into a set from the perspective of semantic mathematics because they share the same basic semantics.

2.4.2 Semantic Mathematical processing of information

Information in semantic mathematics corresponds to different semantic expressions. Through a specific purpose, information links different data, knowledge or wisdom, resulting in new semantic associations. For example, the "depression" of patients with depression is actually a comparison with their past emotional state, which can be analyzed and understood more accurately in semantic mathematics.

2.4.3 Complete semantic analysis of knowledge

In semantic mathematics, knowledge is a complete understanding of the world obtained through observation and learning. For example, the concept of "swans are all white" is a complete semantic induction of a large number of observation results, which can be systematically analyzed and verified under the framework of semantic mathematics.

2.4.4 Semantic Mathematical understanding of wisdom

Wisdom is understood in semantic mathematics as containing advanced information in ethics, social morality and so on. By integrating data, information and knowledge, wisdom can be used to guide more complex decision-making processes in semantic mathematics.

2.4.5 Binary analysis of purpose

Purpose is regarded as a binary group (input, output) in semantic mathematics, in which both input and output can be data, information, knowledge and wisdom. Semantic mathematics enables us to analyze and process these inputs and outputs more accurately in order to achieve the preset goals.

Semantic mathematics realizes the objectification of subjective understanding through the transformation from conceptual space to semantic space, thus improving the accuracy and consistency of understanding and communication.

2.4.6 Semantic transformation of data

Data is the basis of information processing, but it often lacks clear semantic orientation. Through semantic mathematics, we can transform data from a simple factual description into an expressive form with clear semantics. For example, through the method of semantic mathematics, temperature data can be transformed into specific instructions for climate change, thus providing a basis for further analysis and decision-making.

2.4.7 In-depth interpretation of information

The transformation of information level is particularly critical in semantic mathematics. Semantic mathematics not only helps to identify different semantics in information, but also reveals the deep-seated relationship behind these semantics. For example, by analyzing economic data, semantic mathematics can help us understand the complex relationship between different economic indicators, thus providing reference for the formulation of economic policies.

2.4.8 Complete construction of knowledge

Knowledge construction occupies a core position in semantic mathematics. It not only pays attention to the collection of single facts, but also pays attention to the understanding of the complete semantics behind these facts. By connecting scattered information and data, a complete knowledge system is constructed, and semantic mathematics provides a tool for understanding complex systems in depth.

2.4.9 Semantic application of wisdom

The application of semantic mathematics at the level of wisdom pays more attention to values and ethical principles. It not only helps us understand the content of wisdom, but also guides us how to apply it to solve practical problems. For example, in the face of moral dilemma, semantic mathematics can help us analyze the problem from an ethical perspective and find a solution to balance the interests of all parties.

2.4.10 Clarification of purpose

At the level of purpose, semantic mathematics enables us to define and understand goals (outputs) and methods (inputs) more clearly. This clarity is very important in decision-making and problem solving. For example, when designing an artificial intelligence system, semantic mathematics can help us define the goals of the system and the ways to achieve them more accurately.

The application of semantic mathematics in DIKWP model not only redefines our understanding of these concepts, but also provides new tools and methods for dealing with complex data and information. This interdisciplinary integration provides us with a new perspective and methodology in scientific research and technological innovation. Through semantic mathematics, we can understand and manipulate data, information, knowledge, wisdom and purpose more deeply, and then promote the development of knowledge and the application of wisdom.

The application of semantic mathematics in DIKWP model has opened up a new field of cognition and understanding. By transforming data, information, knowledge, wisdom and purpose into clear semantic content, semantic mathematics not only improves our understanding of these concepts, but also enhances our accuracy and consistency in dealing with complex problems. This interdisciplinary integration is expected to play an important role in future scientific research and technological innovation and provide us with more accurate and comprehensive cognitive tools.

2.5 Semantic Mathematics: deep transformation of DIKWP model

The innovation of semantic mathematics lies in transforming DIKWP model from traditional concept space to richer semantic space, so as to realize objectification of subjective understanding and improve the accuracy and consistency of understanding and communication.

2.5.1 Semantic transformation of data

Semantic reconstruction of data: Traditionally, data are regarded as primitive and unprocessed facts. Semantic mathematics makes data become the cornerstone of information processing by giving it clear semantic meaning. This involves not only the classification and labeling of data, but also the mining of the deep meaning behind the data.

Case study: Considering weather data, traditional processing methods may only focus on temperature, humidity and other values. Semantic mathematics may interpret these data as indicators of climate change, so as to understand and apply the data at a deeper level.

2.5.2 In-depth interpretation of information

Semantic expansion of information: information is usually the direct output or interpretation of data from the traditional perspective. The application of semantic mathematics makes information a semantic bridge to connect various data points, providing a more comprehensive and in-depth understanding.

Example demonstration: In the medical and health field, patients' symptoms (data) can be transformed into information with deep medical significance after semantic mathematics processing, thus guiding more effective diagnosis and treatment.

2.5.3 Complete construction of knowledge

Semantic integration of knowledge: under the influence of semantic mathematics, knowledge is no longer an isolated collection of information, but a complete and internally related semantic network. This makes the construction of knowledge not only a process of collecting information, but also a process of understanding and integrating this information.

Case application: In economics, various economic indicators are integrated and interpreted to form a comprehensive understanding of the economic situation. This understanding is not only based on the data itself, but also includes the relationships and trends between the data.

2.5.4 Semantic application of wisdom

Deep interpretation of wisdom: Wisdom is given a new definition in semantic mathematics, which is not only the accumulation of experience, but also the comprehensive application and judgment of data, information and knowledge in complex situations.

Practical operation: In enterprise management, wisdom is embodied in the in-depth understanding of market dynamics and the formulation of coping strategies, which requires comprehensive consideration of data and information from economic, social and technical aspects.

2.5.5 Clarity of purpose

Semantic definition of purpose: purpose is regarded as a blueprint for goal-oriented action in semantic mathematics. This definition clarifies the input (current situation) and output (expected result) of purpose, making the goal clearer.

Application case: In the field of artificial intelligence, the design and optimization of the algorithm is based on the clear definition of its functional purpose, which ensures that the algorithm can achieve the set goals efficiently.

The application of semantic mathematics in DIKWP model realizes the deep transformation from conceptual space to semantic space. This transformation not only enhances the depth and breadth of understanding, but also enhances the accuracy and efficiency of communication.

2.6 Application of Semantic Mathematics in the transformation, compensation and verification of DIKWP resources

DIKWP model (data, information, knowledge, wisdom, purpose) represents five levels of information processing, from basic data to the highest level of purpose and purpose. In the information society, we increasingly need to deal with a large number of information resources in order to acquire knowledge, wisdom and the ability to achieve specific goals. In order to better understand, analyze and apply these resources, semantic mathematics technology has become a key tool, which can transform DIKWP resources from the traditional conceptual space into a richer, more objective and more consistent semantic space. This technical report will focus on how to implement it through three key technologies with the help of semantic mathematics: transforming concepts from conceptual space to semantic space, realizing semantic fusion processing between DIKWP resources, and checking the mutual semantics between resources, so as to improve the processing accuracy, consistency, correctness and completeness of DIKWP resources as a whole.

Semantic transformation of DIKWP resources

DIKWP resources include data, information, knowledge, wisdom and purpose, which respectively represent different levels of information processing. In the traditional concept space, these resources usually exist in the form of text, numbers and symbols, and their meanings and connotations may vary with individuals, backgrounds and contexts. In order to achieve more objective and consistent resource processing, we need to transform these resources from conceptual space to semantic space, that is, give them clear semantic meaning and connotation.

2.6.1 Technical implementation 1: semantic transformation of 1：DIKWP resources

Semantic mathematics technology maps DIKWP resources from traditional concept space to semantic space by establishing a clear semantic model. The key to this transformation is to give each resource a clear semantic label and definition, so that it no longer depends on individual subjective understanding, but is based on a shared and objective semantic framework. This helps to eliminate ambiguity and ensure that everyone can understand and apply these resources in the same way.

Semantic transformation of data: traditionally, the original data may be just a group of numbers or symbols, and it is difficult to understand its actual meaning. Through semantic mathematics technology, data can be given specific semantic tags, for example, temperature data can be interpreted as indicators of climate change. This makes the data easier to understand and apply without interference from individual subjective interpretation.

Deep interpretation of information: In information processing, the meaning of information is often limited by the expression of text or symbols. Semantic mathematics technology can transform information into semantic entities with deeper meaning, for example, transforming the description of medical symptoms into medical terms, which is helpful for more accurate medical diagnosis and treatment.

Complete semantics of knowledge: knowledge is usually scattered pieces of information, and it is difficult to form a complete semantic network. Through semantic mathematics, knowledge can be integrated and interpreted to form a complete semantic network connecting different data and information, which is helpful to understand complex problems more comprehensively.

Deep interpretation of wisdom: wisdom is not only the accumulation of experience, but also the comprehensive application and judgment of data, information and knowledge. Semantic mathematics technology is helpful to understand the meaning of wisdom more deeply and make comprehensive decisions and thinking based on objective semantic framework.

Objective definition of purpose: purpose is no longer an ambiguous goal, but a semantic entity with clear input and output. This definition clarifies the path and goal of purpose and makes the goal clearer.

Semantic fusion processing between DIKWP resources

DIKWP resources are often interrelated and need semantic fusion to understand and apply them more comprehensively. Semantic fusion involves the establishment of semantic relations between different resources, so that they can complement and enrich each other, not just exist in isolation.

DIKWP model represents five levels of information processing, from basic data to the highest level of purpose and purpose. In the information age, we need to deal with a large number of information resources in order to acquire knowledge, wisdom and the ability to achieve specific goals. In order to better understand, analyze and apply these resources, semantic mathematics technology has become a key tool. This section will deeply discuss the implementation of the first key technology, that is, the process of transforming DIKWP resources from traditional conceptual space to semantic space, and its application and influence in different resource types.

Semantic transformation of DIKWP resources

The semantic transformation of DIKWP resources refers to the process of mapping these resources from the traditional conceptual space to a richer, more objective and consistent semantic space. The goal of this transformation is to give each resource a clear semantic label and definition, so that it no longer depends on individual subjective understanding, but is based on a shared and objective semantic framework. This transformation helps to eliminate ambiguity and ensure that everyone can understand and apply these resources in the same way.

Technical background

The emergence of semantic mathematics technology provides an effective tool and method for the semantic transformation of DIKWP resources. Semantic mathematics is a mathematical discipline that studies symbols and language meanings. It focuses on how to transform languages, symbols and concepts into mathematical objects for accurate processing and reasoning. The following are some key aspects of using semantic mathematics technology in the semantic transformation of DIKWP resources:

Establishment of semantic model: Semantic mathematics technology allows us to establish a clear semantic model and express the semantic meaning of resources as a mathematical structure. These model are usually based on theories such as formal logic, ontology and semantic network, which are helpful to capture the relationship and meaning between resources.

Giving semantic tags: Under the framework of semantic mathematics, each DIKWP resource can be given specific semantic tags, which define the meaning, attributes and relationships of the resource. This helps to liberate resources from individual subjective understanding and make them objective.

Semantic mapping: Semantic mathematics technology allows us to formalize the semantic mapping between resources to capture the association and interaction between resources. This is helpful for semantic fusion between resources.

Ambiguity elimination: Semantic mathematics technology can help identify and eliminate ambiguity in resources and ensure that the meaning of resources can be accurately explained in different contexts.

Semantic transformation of data

In the DIKWP model, data is the most basic resource level, which usually exists in the form of numbers, symbols or original observations. Semantic transformation of data is a process of giving these original data clear semantic meaning and connotation, making it easier to understand and apply.

Application case

Consider a temperature data set generated by a weather sensor. In the traditional concept space, these data may be just a set of numbers, such as "28.5°C" or "72°F". However, through semantic mathematics technology, these data can be given specific semantic labels, such as "temperature", and their meanings can be defined, that is, indicating the heat or coldness of the environment. In this way, people can understand the actual meaning of these data without knowing the unit or symbol of temperature deeply.

This semantic transformation makes data easier to be applied in different contexts. For example, these temperature data can be used in meteorological prediction, climate research, architectural design and other fields without in-depth understanding of the specific units or sources of each data point.

Influence and advantage

Semantic transformation of data brings multiple advantages:

Reduce ambiguity: Semantic data are not easily disturbed by individual subjective understanding because they have clear semantic labels. This helps to reduce the ambiguity in data interpretation.

Improve data usability: semantic transformation of data makes data easier to understand and apply, thus improving data usability. This is very important for data-driven decision-making and analysis.

Promote cross-domain application: Semantic data can be more widely used in different fields, because their meanings are not limited by specific fields. This promotes interdisciplinary research and knowledge sharing between fields.

Improve data integration: Semantic data from different data sources are easier to integrate and correlate, thus forming more comprehensive cognitive data.

Semantic transformation of data is the first step in DIKWP resource processing, which lays the foundation for semantic transformation of information, knowledge, wisdom and purpose at a higher level.

In-depth interpretation of information

Information is the second level in the DIKWP model, which usually exists in the form of text, symbols or numbers. The semantic transformation of information is a process of giving these information deeper meaning for more accurate interpretation and analysis.

Application case

Consider a medical report that describes the symptoms and diagnosis of patients. In the traditional concept space, these descriptions may only be some text fragments, such as "headache", "fever" and "diagnosis of influenza". However, through semantic mathematics technology, these descriptions can be transformed into semantic entities of medical terms, such as "headache" can be associated with "head pain", "fever" can be associated with "elevated body temperature" and "flu" can be associated with "cold virus infection".

This semantic transformation makes medical reports easier to interpret and analyze, and doctors and researchers can directly relate them to medical knowledge without guessing the meaning of symptoms.

Influence and advantage

The semantic transformation of information brings multiple advantages:

Improve the accuracy of interpretation: Semantic information is easier to interpret accurately because they are associated with clearly defined semantic entities. This is helpful to the accuracy of medical diagnosis and scientific research.

Support automatic processing: Semantic information can be more easily understood and processed by computer programs, thus supporting automatic analysis and decision-making.

Promote knowledge sharing: Semantic information can cross language and cultural barriers and promote international knowledge sharing and cooperation.

Support semantic search: Semantic information can be easily indexed and retrieved by search engines and information retrieval systems, thus improving the accessibility of information.

Semantic transformation of information not only helps individuals to better understand information, but also provides a more powerful tool for information processing and analysis.

Complete semantics of knowledge

Knowledge is the third level in DIKWP model, which is usually expressed as a set of known facts, rules or concepts. Semantic transformation of knowledge is a process of integrating and interpreting these knowledge fragments to form a complete semantic network connecting different data and information.

Application case

Consider a knowledge base that contains information about historical events, scientific theories and cultural knowledge. In the traditional concept space, these knowledge fragments may be scattered and lack of clear relationship. However, through semantic mathematics technology, these knowledge fragments can be integrated and connected to form a complete semantic network. For example, knowledge base can associate different historical events with time axis, scientific theories with related experiments and discoveries, and cultural knowledge with historical background and influence.

This semantic transformation makes knowledge easier to understand, not only to find specific information, but also to understand the logic and relationship behind knowledge.

Influence and advantage

The complete semantic transformation of knowledge brings multiple advantages:

Improve knowledge integration: A complete semantic knowledge network helps to integrate knowledge from different fields and sources to form more comprehensive cognitive materials.

Support interdisciplinary research: A complete knowledge network promotes interdisciplinary research and the handling of complex problems, because they capture the relationships and connections between different fields.

Enhance knowledge discovery: A complete knowledge network can support knowledge discovery and insight, because they reveal the patterns and laws hidden behind knowledge.

Promote intelligent decision-making: A complete knowledge network helps intelligent systems to make more complicated decisions and reasoning, because they provide more background and information.

The complete semantic transformation of knowledge transforms knowledge from fragmented information fragments into organic knowledge networks, which provides a stronger foundation for knowledge management and application.

The semantic transformation of DIKWP resources is the key step to transform information from the traditional conceptual space into a richer, more objective and consistent semantic space. Through semantic mathematics technology, data, information and knowledge can be given clear semantic labels and definitions, making them easier to understand and apply. This transformation has brought multiple advantages, including reducing ambiguity, improving data availability, supporting cross-domain applications, promoting knowledge sharing, improving interpretation accuracy, supporting automatic processing, promoting knowledge sharing, supporting semantic search, improving knowledge integration and supporting interdisciplinary research.

2.6.2 Technology implementation 2: Semantic fusion processing between DIKWP resources

Semantic mathematics technology promotes the semantic fusion between resources by establishing the semantic relationship between resources. This includes the classification, correlation and complementarity of resources, thus enhancing the overall richness and coherence of resources.

Fusion of data and information: The semantic relationship between data and information can help to integrate different data points and information fragments into more comprehensive cognitive materials. For example, in weather forecast, combining temperature data with rainfall information is helpful to provide more comprehensive weather forecast.

Construction of knowledge network: knowledge is usually scattered in different fields and sources. Through semantic mathematics, knowledge in different fields can be interrelated and supplemented to form a more complete knowledge network. This is helpful for interdisciplinary research and the handling of complex problems.

Comprehensive application of wisdom: wisdom needs to comprehensively consider multiple resources, and semantic mathematics technology can help to comprehensively apply the wisdom of different resources to specific problems. In enterprise management, we should not only consider market data, but also consider social, technical and other information in order to make a more comprehensive decision-making strategy.

Mutual semantic check between DIKWP resources

In order to ensure the overall processing accuracy, consistency, correctness and integrity of DIKWP resources, we need to check the mutual semantics between resources. This means that the semantic relationship between different resources needs to be verified and proofread to eliminate possible errors and inconsistencies.

Semantic fusion processing between DIKWP resources

Semantic fusion between DIKWP resources involves establishing semantic relations between different resources in order to understand and apply them more comprehensively. This treatment is to ensure that resources can complement and enrich each other, not just exist in isolation. Semantic fusion processing is helpful to integrate DIKWP resources into a more valuable whole, making it more comprehensive, coherent and deep.

Technical background

The realization of semantic fusion processing needs to establish clear semantic model and define the semantic relations between resources in these model. The following are some key technologies used in semantic fusion processing of DIKWP resources:

Establishment and extension of semantic model: The establishment and extension of semantic model is the key step to formalize the semantic relationship between resources. This involves the use of formal logic, ontology, semantic network and other theories to describe the relationship and meaning between resources.

Establishment of semantic relationship: semantic relationship is the relationship between resources, which can be hierarchical relationship, relational relationship, dependency relationship and so on. The establishment of these relationships is conducive to the complementarity and enrichment of resources.

Semantic mapping and transformation: formalize the semantic mapping and transformation between different resources to ensure that they can complement and enrich each other. This includes the process of transforming data into information, information into knowledge, and knowledge into wisdom.

Cross-resource semantic query: Cross-resource semantic query allows users to search and query semantically between different resources to obtain more comprehensive information. This is helpful to the comprehensive application and in-depth excavation of resources.

Application cases

The following are some examples to illustrate the application and influence of semantic fusion processing between DIKWP resources in different fields:

Health care field: In the medical field, semantic fusion of patients' clinical data, medical knowledge and treatment guidelines will help doctors to evaluate patients' health status more comprehensively and formulate personalized treatment plans. For example, the symptoms of patients are associated with medical knowledge to support clinical decision-making.

Financial field: In the financial field, semantic integration of market data, financial reports and economic indicators will help investors to evaluate risks and opportunities more accurately. For example, different market indicators are associated with corporate financial data for portfolio management.

Scientific research: In scientific research, the semantic fusion of experimental data, scientific literature and domain knowledge will help researchers better understand complex scientific problems. For example, the experimental results are linked with relevant scientific theories and documents to promote new discoveries.

Intelligent decision-making system: In intelligent decision-making system, semantic fusion of different data, information and knowledge is helpful for the system to make more accurate decisions and reasoning. For example, market data, social trends and technological development are associated with decision-making rules to support automated decision-making.

Effects and advantages

The implementation of semantic fusion processing has brought multiple advantages:

Comprehensiveness: Semantic fusion enables different resources to complement and connect with each other to form a more comprehensive whole. This helps to understand and apply resources more comprehensively.

Coherence: By establishing semantic relations, the association between resources becomes more coherent and consistent. This helps to eliminate inconsistencies and contradictions and improve the consistency of resource processing.

Depth: semantic fusion processing makes the relationship between resources deeper and deeper. This is helpful to reveal the hidden patterns and laws between resources and promote the in-depth mining of knowledge.

Wide application: resources processed by semantic fusion can be used more widely in different fields and applications, because they are not limited by specific fields.

Support decision-making: semantic fusion processing is helpful for intelligent decision-making system to make more complicated decisions and reasoning, because it provides more background information and correlation.

Semantic fusion processing is a key step to transform DIKWP resources into a more valuable whole, which provides important support for comprehensive application and in-depth analysis of resources.

The semantic fusion of DIKWP resources is the key link of information processing, which involves establishing the semantic relationship between different resources to form a more valuable, comprehensive and in-depth overall resource. Through semantic mathematics technology, the relationship and meaning between resources are clarified, and the processing accuracy, consistency and coherence of resources are optimized. This technology not only plays an important role in theoretical research, but also brings great changes and progress in practical application in various fields. With the help of semantic fusion, we can better understand, analyze and apply DIKWP resources, and promote greater breakthroughs in scientific research and technological innovation. In the future, with the continuous development of semantic mathematics technology, we can foresee that it will play an increasingly important role in various fields and provide more accurate, consistent and in-depth knowledge and wisdom resources for the information society.

2.6.3 Technical implementation 3: mutual semantic check between 3：DIKWP resources

Semantic mathematics technology can help to check the mutual semantics between resources and ensure the accuracy and consistency of resources.

Verification of data and information: the semantic relationship between data and information needs to be verified to ensure the accuracy of data and the correct interpretation of information. In scientific research, it is very important to ensure the consistency of experimental data and related information for the reliability of research results.

Verification of knowledge network: the semantic relationship between different knowledge points in knowledge network needs to be verified to ensure the correctness and integrity of knowledge. In the medical field, the logical relationship between knowledge points needs to be strictly verified to ensure the accuracy of medical diagnosis and treatment.

Review of wisdom: the comprehensive application of wisdom needs to be reviewed and verified to ensure the correctness and effectiveness of the comprehensive application. In policy making, the logic and data of intelligent decision-making need to be audited to ensure the rationality and feasibility of the policy.

Complete semantic transformation of knowledge

In DIKWP model, knowledge represents a set of known facts, rules and concepts. The complete semantic transformation of knowledge is the process of integrating and interpreting these knowledge fragments to form a complete semantic network connecting different data and information. This process not only contributes to the integration of knowledge, but also helps people to better understand the logic and relationship behind knowledge and promote the discovery and application of knowledge.

Technical background

The complete semantic transformation of knowledge needs a series of advanced semantic mathematics techniques and knowledge engineering methods. The following are the key technologies used in this process:

Ontology and knowledge map: Ontology is a subject that studies concepts, entities and their relationships. In the complete semantic transformation of knowledge, ontology is widely used to construct ontology and describe the hierarchical relationship between concepts, attributes and associations between entities. Knowledge map is the actual representation of ontology, which presents knowledge in the form of graph, making the relationship between different entities and concepts visible and operable.

Natural Language Processing (NLP): NLP technology is used to process and understand natural language texts, which helps to transform text information into operational knowledge. This includes the tasks of text syntax analysis, semantic analysis, named entity identification and so on, so as to map text information to concepts and entities in ontology and knowledge map.

Knowledge representation and reasoning: knowledge representation allows knowledge to be represented in a machine-readable form, such as logical representation, triple representation, etc. Inference technology is used to deduce new knowledge from existing knowledge. These methods are helpful to enrich and deepen knowledge.

Data mining and machine learning: Data mining and machine learning techniques are used to discover patterns and laws hidden in knowledge. This can help to dig deeper into knowledge and discover new connections.

Application case

The following are some examples to illustrate the application and influence of complete semantic transformation of knowledge in different fields:

Education: In the field of education, the complete semantic transformation of educational resources, subject knowledge and students' performance is helpful to build an intelligent education system. These systems can provide personalized educational content and suggestions according to students' learning styles and needs.

Enterprise knowledge management: Enterprises can complete semantic transformation of internal knowledge resources, such as employee training materials, project documents and lessons learned, so as to establish knowledge base and intelligent search system. This helps employees get the information they need faster and improve their work efficiency.

Scientific research: In scientific research, the complete semantic transformation of experimental data, scientific literature and domain knowledge will help researchers better understand the development and trends in the field. This can promote the further discovery and innovation of knowledge.

Medical field: in the medical field, the complete semantic transformation of case data, medical knowledge and treatment guidelines will help doctors to evaluate patients' condition and make treatment plans more comprehensively. This can improve the accuracy and efficiency of medical decision.

Effects and advantages

The complete semantic transformation of knowledge brings multiple advantages:

Knowledge integration: A complete and semantically transformed knowledge network helps to integrate knowledge from different fields and sources to form more comprehensive cognitive materials. This is helpful to enrich and deepen knowledge.

Interdisciplinary research: A complete semantic knowledge network promotes interdisciplinary research and the handling of complex problems, because they capture the relationships and connections between different fields.

Enhance knowledge discovery: The knowledge network with complete semantic transformation supports knowledge discovery and insight, because they reveal the patterns and laws hidden behind knowledge.

Promote intelligent decision-making: The knowledge network with complete semantic transformation is helpful for intelligent systems to make more complicated decisions and reasoning, because they provide more background and information.

Knowledge sharing and cooperation: a complete semantic knowledge network can cross language and cultural barriers and promote international knowledge sharing and cooperation.

2.6.4 Future Outlook

The complete semantic transformation of knowledge is the key link of knowledge management and application in the information society. With the continuous development of semantic mathematics technology, we can expect more innovative applications and wider field coverage. Future development trends may include:

Intelligent decision support: the knowledge network with complete semantic transformation will become an important part of intelligent system, supporting more complex and higher-level decision-making and reasoning.

Global knowledge sharing: through complete semantic transformation, knowledge can more easily cross the boundaries of language and culture, and promote global knowledge sharing and cooperation.

Education and training: A complete semantic knowledge network can be used to build a more intelligent and personalized education and training system and improve learning efficiency.

Medical diagnosis and treatment: In the medical field, the complete semantic transformation of knowledge will help to develop more accurate and personalized diagnosis and treatment methods.

In short, the complete semantic transformation of knowledge is an important technology in the information age, which not only contributes to the enrichment and deepening of knowledge, but also supports the discovery, application and sharing of knowledge. With the continuous progress of technology, we can expect more innovative applications and provide more possibilities for social development and progress. This technology will continue to push us towards smarter and more effective knowledge management and application.

Semantic mathematics technology plays an important role in DIKWP resource processing. By transforming resources from conceptual space to semantic space, semantic fusion processing between resources and mutual semantic verification between resources are realized, the processing accuracy, consistency, correctness and integrity of the whole resources are improved. This technology is not only of great significance in theoretical research, but also brings great changes and progress in practical applications in various fields. With the help of semantic mathematics, we can better understand, analyze and apply DIKWP resources and promote greater breakthroughs in scientific research and technological innovation. In the future, with the continuous development of semantic mathematics technology, we can foresee that it will play an increasingly important role in various fields and provide more accurate, consistent and complete knowledge and wisdom resources for the information society.

2.7 Semantic Mathematics helps DIKWP to realize subjective objectivity

Under the framework of semantic mathematics, DIKWP model has undergone a transformation from traditional concept understanding to semantic objectification. The core of this transformation is to reduce or eliminate the influence of subjective semantics and achieve a more objective and consistent understanding.

2.7.1 Objective semantics of data

Semantic mathematics regards data as an entity with specific objective semantics, not just a collection of original facts. This transformation means that the interpretation of data is no longer limited by individual subjective understanding, but based on a shared and objective semantic framework.

2.7.2 Objectification of information processing

Information is further processed and refined through the semantic mathematical framework and transformed into knowledge with objective semantics. This processing method reduces the subjective variation of information in individual cognition and promotes the accurate transmission and application of knowledge.

2.7.3 Objective construction of knowledge

Semantic mathematics promotes the abstraction of objective and complete semantics from data and the construction of a consistent knowledge system. This process reduces the influence of individual cognitive differences in knowledge construction and improves the universality and effectiveness of knowledge.

2.7.4 Objective interpretation of wisdom

Wisdom is interpreted as the objective comprehensive application and judgment of data, information and knowledge in semantic mathematics. This interpretation reduces the subjectivity of values and ethical principles at the individual level and promotes more objective decision-making and thinking.

2.7.5 Objective definition of purpose

Purpose is defined as an action plan with clear objective goals and paths under the framework of semantic mathematics. This definition reduces the subjectivity in the process of achieving the goal and ensures the purpose and effectiveness of the action.

Through the application of semantic mathematics, every link of DIKWP model has realized the transformation from concept to objective semantics, thus making the whole process not only more efficient, but also more purposeful and accurate. This method provides a brand-new framework and methodology for modern scientific research and technological innovation, especially in dealing with complex problems and promoting effective communication. By reducing the influence of subjective semantics, semantic mathematics ensures the consistency and universality of knowledge and provides a common understanding foundation for people from different fields and backgrounds.

2.8 Role of Essence Computation and Reasoning in DIKWP model

This chapter will discuss in detail the Essence Computation and Reasoning in DIKWP model, and explain the application of semantic mathematics technology at this level. Essence Computation and Reasoning represent the first level of DIKWP model and are the most basic part of cognitive process. At this level, the individual aims to understand the essence and basic attributes of things, which involves the acquisition and processing of data and the process of transforming information into knowledge. Through the application of semantic mathematics technology, we can make Essence Computation and Reasoning more objective and accurate, and ensure that the interpretation of data is no longer limited by individual subjective understanding, but based on a shared and objective semantic framework, thus improving accuracy and consistency.

2.8.1 Definition of Essence Computation and Reasoning

Essence Computation and Reasoning is the first level in DIKWP model, which represents the most basic part of cognitive process. At this level, individuals try to understand the essence and basic attributes of things, including their characteristics, attributes and essence attributes. Essence Computation and Reasoning involve the following main aspects:

Acquisition and processing of data: individuals need to collect data, which can be information from perception, observation or other sources. These data are the starting point of cognition, and they need to be processed and analyzed to extract the basic information.

Information extraction: In the process of data processing, individuals need to extract information related to the problem from the data. This information includes the attributes, characteristics and relationships of things, and is used for further cognition and reasoning.

Transformation of information into knowledge: Essence Computation and Reasoning also include the process of transforming information into knowledge. This means integrating the extracted information into the existing knowledge system in order to better understand and explain the essence of things.

Identification of basic attributes: The ultimate goal is to identify the basic attributes of things, including their core features and attributes. This helps individuals to better understand the nature of things.

2.8.2 Application of Semantic Mathematics in Essence Computation and Reasoning

Objective Semanticization of Data

The first application of semantic mathematics is to make data objectively semantic. Traditionally, data is regarded as a collection of original facts, and its interpretation is limited by individual subjective understanding. However, under the framework of semantic mathematics, data is regarded as an entity with specific objective semantics. This transformation means that the interpretation of data is no longer only dependent on individual's subjective understanding, but based on a shared and objective semantic framework. This helps to eliminate the influence of individual subjectivity on data interpretation, thus making Essence Computation and Reasoning more objective and consistent.

Objectification of information processing

Information plays a key role in Essence Computation and Reasoning, because it contains the attributes, characteristics and relationships of things. The application of semantic mathematics transforms information into knowledge with objective semantics by further processing and refining it. This processing method reduces the subjective variation of information in individual cognition and ensures the accurate transmission and application of information. The objectification of information is helpful for individuals to better understand and reason the essence attributes of things, and it is also helpful for sharing and cooperation between different individuals.

The objective construction of knowledge

The ultimate goal of Essence Computation and Reasoning is to construct an objective cognition of the essence of things, including their basic attributes and characteristics. Semantic mathematics promotes the abstraction of objective and complete semantics from data and the construction of a consistent knowledge system. This process reduces the influence of individual cognitive differences in knowledge construction and improves the universality and effectiveness of knowledge. The objective construction of knowledge helps individuals to better understand and explain the essence of things, and also provides a solid foundation for the sharing and dissemination of knowledge.

2.8.3 Example: Application of Semantic Mathematics in Scientific Research

Let's illustrate the application of semantic mathematics in Essence Computation and Reasoning through an example. Consider an astronomer studying galaxies. The research involves a large number of astronomical observation data, including the location, brightness, color and other information of galaxies. In traditional data analysis, different researchers may interpret and process the data differently according to their subjective understanding, which leads to inconsistency and misunderstanding of the results.

However, by applying semantic mathematics technology, these data can be semanticized objectively. For example, semantic mathematics can define the shared semantic framework of galaxy attributes, so that different researchers can understand and interpret data in the same way. This helps to reduce the influence of subjectivity and ensure that the interpretation of data is more objective and consistent. In addition, semantic mathematics can also help to combine data with the existing astronomical knowledge system to build more complete and accurate knowledge. Finally, astronomers can better understand the essence properties of galaxies and infer their evolution process, thus promoting greater breakthroughs in scientific research.

This chapter discusses in detail the Essence Computation and Reasoning in DIKWP model, and expounds the application of semantic mathematics technology at this level. Essence Computation and Reasoning represent the most basic part of cognitive process, which involves the process of obtaining and processing data and transforming information into knowledge. Through the application of semantic mathematics technology, we can achieve more objective and accurate Essence Computation and Reasoning, and ensure that the interpretation of data is no longer limited by individual subjective understanding, but based on a shared and objective semantic framework. This change is helpful to improve the accuracy and consistency of cognition and promote greater breakthroughs in scientific research and technological innovation. In the future, semantic mathematics technology will continue to play an important role in different fields, providing strong support for cognition and reasoning.

2.9 Role of Existence Computation and Reasoning in DIKWP model

Existence Computation and Reasoning are the second level in DIKWP model, which represents the cognition of existence. At this level, individuals try to understand the existence and relativity of things and associate information with existing cognitive objects. Existence Computation and Reasoning involve matching information with known facts and treating them as the same object or concept. This chapter will discuss in detail the existence Computation and Reasoning in DIKWP model, and explain the application of semantic mathematics technology at this level.

2.9.1 Definition of Existence Computation and Reasoning

Existence Computation and Reasoning is the second level in DIKWP model, which represents the cognition of existence in the cognitive process. At this level, individuals try to understand the existence and relativity of things and associate information with existing cognitive objects. Existence Computation and Reasoning include the following main aspects:

Confirmation of the existence of information: individuals need to determine whether the things involved in information exist. This may involve verifying the source and authenticity of information to ensure that the information describes the existence in reality.

Association and matching of information: Existence Computation and Reasoning also include the process of association and matching of information with existing cognitive objects. Individuals need to match newly acquired information with known concepts, objects or facts for further cognition and reasoning.

Classification and organization of information: In order to better understand the existence and relativity of things, individuals need to classify and organize information. This helps to establish a cognitive framework and make information easier to understand and apply.

Relativity of existence: in the Computation and Reasoning of existence, individuals also need to consider the relativity of existence. That is, whether the information is consistent with the known facts or contradicts other information, thus forming a relative cognition.

2.9.2 Application of Semantic Mathematics in Existence Computation and Reasoning

Objectification of information processing

Semantic mathematics technology further processes and refines information by transforming information into knowledge with objective semantics. This helps to ensure the accurate transmission and application of information in existence Computation and Reasoning. The objectification of information reduces the subjective variation of information in individual cognition by matching the information with the existing semantic framework. This helps to ensure that information is consistent with the existence of the real world and improves the reliability of cognition.

Association and matching of information

In Existence Computation and Reasoning, semantic mathematics technology can help individuals to associate and match newly acquired information with known cognitive objects. By establishing a shared semantic framework, different individuals can associate information and cognitive objects in the same way. This helps to avoid misunderstanding and confusion and ensure that information is correctly related to the corresponding concepts or objects. Semantic mathematics technology can provide an objective way to perform these association and matching operations.

Classification and organization of information

The classification and organization of information plays a key role in Existence Computation and Reasoning. Through semantic mathematics technology, information can be better classified and organized to establish a cognitive framework. This makes it easier for individuals to understand the relativity of information and put it into relevant cognitive context. The objectivity of classification and organization is helpful for better understanding and application of information.

2.9.3 Example: application of Semantic Mathematics in medical diagnosis

Let's illustrate the application of semantic mathematics in Existence Computation and Reasoning through an example. Consider the situation of a doctor in diagnosing patients. Doctors need to collect patients' symptom information and match it with known medical knowledge to determine possible diagnosis. In traditional diagnosis, different doctors may associate symptoms and diagnosis according to their individual subjective understanding, which leads to the risk of misdiagnosis.

However, by applying semantic mathematics technology, doctors can objectively relate symptom information to the known medical knowledge framework. This framework defines the semantic relationship between symptoms and possible diagnosis, thus ensuring the objectivity and consistency of diagnosis. Semantic mathematics technology can also help doctors classify and organize symptom information in order to better understand the patient's situation. In the end, doctors can diagnose more reliably and reduce the risk of misdiagnosis.

This chapter discusses the existence Computation and Reasoning in DIKWP model in detail, and expounds the application of semantic mathematics technology at this level. Existence Computation and Reasoning represent the cognition of existence in the cognitive process, including the confirmation of information existence, the association and matching of information, the classification and organization of information, and the relativity of existence. Through the application of semantic mathematics technology, we can achieve more objective and accurate existence Computation and Reasoning, ensure the accurate transmission and application of information, reduce the risk of misunderstanding and confusion, and improve the reliability of cognition. In the future, semantic mathematics technology will continue to play an important role in various fields, providing strong support for cognition and reasoning.

2.10 Role of purpose Computation and Reasoning in DIKWP model

purpose Computation and Reasoning represent the fifth level of DIKWP model, which involves the formulation and implementation of action plan. At this level, individuals achieve clear goals by dealing with and solving specific problems or phenomena. purpose Computation and Reasoning include defining input and output, and making the output gradually approach the preset goal through learning and adaptation. This chapter will discuss the purpose Computation and Reasoning in DIKWP model in detail, and explain the application of semantic mathematics technology at this level.

2.10.1 Definition of purpose Computation and Reasoning

purpose Computation and Reasoning is the fifth level in DIKWP model, which represents decision-making and action-making in cognitive process. At this level, individuals try to solve problems or achieve clear goals, and achieve these goals through planning and action. purpose Computation and Reasoning include the following main aspects:

Clear goals: Individuals need to clearly define their goals or objectives. This can be solving a specific problem, completing a task, realizing a project, etc. The clarity of the goal is very important for effective purpose Computation and Reasoning.

Definition of input and output: purpose Computation and Reasoning require individuals to clearly define input and output. Input refers to the description of the problem or situation, while output refers to the expected result or solution. Defining inputs and outputs helps individuals better understand problems and goals.

Plans and strategies: In order to achieve goals, individuals need to make plans and strategies. This includes determining appropriate action steps, using resources and making decisions. Planning and strategy are the key to achieving the goal.

Learning and adaptation: purpose Computation and Reasoning also include the process of learning and adaptation. Individuals need to constantly learn from experience and adjust plans and strategies to achieve their goals more effectively. Adaptability is an important part of purpose Computation and Reasoning.

2.10.2 Application of Semantic Mathematics in purpose Computation and Reasoning

Objective definition of objectives

The primary application of semantic mathematics technology in purpose Computation and Reasoning is the definition of objective goals. It helps individuals define goals as action plans with clear objective goals and paths. Through semantic mathematics technology, the definition of goals no longer depends on individual subjective understanding, but is based on a shared and objective semantic framework. This objective method helps to ensure the clarity and effectiveness of the goal and reduces the subjectivity in the process of achieving the goal.

Clear definition of input and output

In purpose Computation and Reasoning, semantic mathematics technology can help individuals clearly define input and output. By matching the input and output with the existing semantic framework, individuals can understand the problems and goals more clearly. This helps to establish a clear plan and action steps to ensure that the realization of the goal is more targeted.

Objectification of plans and strategies

Semantic mathematics technology can also help to make objective plans and strategies. By matching plans and strategies with objective semantic relations, individuals can make action plans more effectively. This will help to improve the feasibility of the plan and the efficiency of achieving the goal.

Support for learning and adaptation

Semantic mathematics technology can be used to support the process of learning and adaptation in purpose Computation and Reasoning. Individuals can learn from historical data and know which strategies and plans are successful and which are unsuccessful. This will help to constantly improve and optimize the way of realizing purposes and improve the chances of success.

2.10.3 Example: application of Semantic Mathematics in project management

Let's illustrate the application of semantic mathematics in purpose Computation and Reasoning through an example. Consider that a project manager is responsible for managing a complex project and needs to ensure that the project is delivered on time. In traditional project management, the project manager may rely on personal experience and subjective judgment to make plans and strategies, which may lead to the risk of project delay or budget overrun.

However, by applying semantic mathematics technology, the project manager can objectively define the objectives, inputs and outputs of the project. Semantic mathematics technology can help project managers clearly define project objectives as action plans with objective objectives and paths. Input and output can also be clearly defined to better understand the requirements and delivery of the project. In addition, semantic mathematics technology can support project managers to make plans and strategies to maximize the efficiency and chances of success of the project. Through the support of learning and adaptation, the project manager can constantly adjust the plan according to the actual situation of the project to ensure the timely delivery of the project.

This chapter discusses the purpose Computation and Reasoning in DIKWP model in detail, and expounds the application of semantic mathematics technology at this level. purpose Computation and Reasoning represent the decision-making and action formulation in the cognitive process, including clear goals, definitions of input and output, formulation of plans and strategies, and the process of learning and adaptation. Semantic mathematics technology provides strong support for purpose Computation and Reasoning by defining objective goals, clearly defining input and output, formulating objective plans and strategies, and supporting learning and adaptation. This objective method is helpful to improve the clarity and effectiveness of the goal, reduce the subjectivity in the process of achieving the goal, and thus improve the quality and efficiency of decision-making and action. In various fields, including project management, decision-making and problem solving, semantic mathematics technology can play a key role in helping individuals better understand and realize their purposes.

2.11 Integration of DIKWP with Essence Computation and Reasoning, Existence Computation and Reasoning, and purpose Computation and Reasoning.

DIKWP model represents a comprehensive cognitive model, covering multiple cognitive levels from data to wisdom. In the DIKWP model, the transformation of resources is an important concept, which involves the process of transforming and applying different types of resources, such as data, information, knowledge, wisdom and purpose. This chapter will discuss the resource transformation in the DIKWP model, and focus on the integration of three key technologies (Essence Computation and Reasoning, Existence Computation and Reasoning, and purpose Computation and Reasoning) to achieve a more efficient cognitive and decision-making process.

2.11.1 Resource transformation in DIKWP model

In the DIKWP model, resource transformation is the core activity in the cognitive process. The transformation of resources involves multi-level processing and reasoning from low-level data to high-level wisdom. The following are the main levels of resource transformation in the DIKWP model:

Essence Computation and Reasoning: Essence Computation and Reasoning represent the most basic cognitive level, in which individuals try to understand the essence and basic attributes of things. At this level, data is transformed into basic information and then further transformed into knowledge. This transformation involves the gradual refining and reasoning process from data to information to knowledge.

Existence Computation and Reasoning: Existence Computation and Reasoning represent the cognition of existence. At this level, individuals try to understand the existence and relativity of things and associate information with existing cognitive objects. This includes matching information with known facts as the same object or concept.

Purpose Computation and Reasoning: purpose Computation and Reasoning represent cognitive decision-making and action formulation. At this level, individuals achieve clear goals by dealing with and solving specific problems or phenomena. purpose Computation and Reasoning include clear objectives, definitions of input and output, formulation of plans and strategies, and the process of learning and adaptation.

2.11.2 Integration of three technologies

In order to realize a more efficient cognitive and decision-making process, the three technologies (Essence Computation and Reasoning, Existence Computation and Reasoning, and purpose Computation and Reasoning) in DIKWP model can be integrated with each other. This integration can promote the more effective transformation and application of resources and improve the quality and efficiency of cognition.

Fusion of Essence Computation and Reasoning and Existence Computation and Reasoning

Essence Computation and Reasoning and Existence Computation and Reasoning can be integrated with each other to realize more comprehensive resource transformation. In this fusion, individuals can first understand the basic attributes and essence of things through the process of Essence Computation and Reasoning, and then associate these essence attributes with existing cognitive objects to identify existence and relativity. This integration helps to improve the profound understanding of things, reduce ambiguity, and ensure the objective confirmation of the existence of things. For example, through Essence Computation and Reasoning, individuals can understand that the essence attribute of swan is white, and then match this essence attribute with the known swan fact through Existence Computation and Reasoning to confirm that an object is a swan.

Integration of Existence Computation and Reasoning and purpose Computation and Reasoning

The integration of Existence Computation and Reasoning and purpose Computation and Reasoning can promote more effective problem solving and decision making. In this fusion, individuals can first confirm the existence of problems and related information through the process of existence Computation and Reasoning, and then use these information in the process of clear goal setting and purpose Computation and Reasoning. This integration helps to ensure that problem solving and decision-making are based on objective information and consistent with known cognitive objects. For example, in project management, individuals can identify the problems and needs in the project through Existence Computation and Reasoning, and then make clear project goals and plans through purpose Computation and Reasoning to solve the problems and achieve the goals.

Fusion of Essence Computation and Reasoning and purpose Computation and Reasoning

The integration of Essence Computation and Reasoning and purpose Computation and Reasoning can promote deeper cognition and more effective action. In this fusion, individuals can first understand the essence attributes and basic attributes of things through the process of Essence Computation and Reasoning, and then use these understandings in the process of clear goal setting and purpose Computation and Reasoning. This kind of integration helps to ensure that the formulation of goals is based on profound understanding and objective essence attributes, thus improving the quality and efficiency of decision-making and action. For example, in scientific research, individuals can deeply understand the essence attributes of physical phenomena through Essence Computation and Reasoning, and then make clear experimental goals and plans through purpose Computation and Reasoning to promote the progress of scientific research.

2.11.3 Advantages and applications of fusion

The integration of the three technologies has important advantages and applications in the DIKWP model:

More comprehensive cognition: A more comprehensive cognitive process can be realized by integrating Essence Computation and Reasoning, Existence Computation and Reasoning, and purpose Computation and Reasoning. Individuals can better understand the essence attributes of things, confirm their existence, and define their goals, so as to better cope with complex cognitive tasks.

More efficient decision-making and action: integration makes the cognitive and decision-making process more efficient. Individuals can set goals based on objective information, reduce subjectivity and ambiguity, and thus improve the quality and efficiency of decision-making and action.

A wide range of applications: this fusion technology can be widely used in different fields, including scientific research, project management, decision-making and so on. It helps to better understand and solve complex problems and promote innovation and progress.

Accumulation and transmission of knowledge: Through integration, knowledge can be better accumulated and transmitted. Individuals can produce deeper knowledge through the process of in-depth understanding and objective confirmation, thus providing valuable information and insights for others.

The resource transformation in the DIKWP model is the key link in the cognitive process, and the integration of three key technologies (Essence Computation and Reasoning, Existence Computation and Reasoning, and purpose Computation and Reasoning) can realize a more efficient and comprehensive cognitive and decision-making process. This kind of integration is helpful to deeply understand the essence attributes of things, confirm their existence and define their goals, thus improving the quality and efficiency of decision-making and action. This fusion technology has a wide range of applications, and is helpful to the accumulation and transmission of knowledge, and promotes innovation and progress. By integrating these technologies, individuals and organizations can better cope with complex cognitive tasks and problems and achieve greater success.

3 The Deep Semantics of Mathematics: Applications and Implications of the New Semantic Mathematics in Science and Technology

New Semantic Mathematics, as an innovation in the field of mathematics, not only redefines our understanding of mathematical objects, but also provides new perspectives and tools for all areas of science and technology. This chapter provides insights into how New Semantic Mathematics goes beyond the limitations of traditional mathematics to reveal deeper relationships and structures among mathematical objects. Particular attention is paid to the transformation of mathematical logic and reasoning within the framework of the new semantic mathematics, with an emphasis on the understanding of the deeper meanings behind mathematical concepts and how these concepts play out in the wider body of knowledge.

3.1 Integer semantics and its role in mathematical reasoning (Semantic Mathematics)

One of the central issues in AI research is how to understand and process semantics, as semantics is not only key to accurately representing information, but also affects the effectiveness of computation and reasoning. This chapter explores the different levels of semantics, focusing on the shift from subjective to objective semantics. While various approaches, such as ontologies and meta-models, have been proposed to deal with semantics, the individualised nature of subjective semantics and the difficulty of grasping it, often hidden behind incomplete forms of expression, make it difficult to be explicitly defined and expressed in terms of concepts. Furthermore, the conversion from subjective to objective semantics usually leads to a distinction between data, which is usually objective, and information, which contains a subjective purpose that involves a non-deterministic choice of multiple potential or uncertain purposes.

For society as a whole, the process of selecting the target semantics among determining multiple possibilities can lead to a huge waste of communication efficiency and effectiveness if we ignore for a moment the beauty of cognitive uncertainty. From a constructive point of view, it would be very useful if we could ideally identify some basic semantics and the vectors or concepts associated with them. In this chapter, we have chosen to explore this issue in depth in the realm of numbers, particularly integers. Because reasoning and computation are essentially about making connections between known or assumed semantics and the unknown, we propose the notion of defining objective semantic computation and reasoning, i.e., studying and facilitating the effective and efficient modelling and manipulation of semantics and their associated concepts.

3.1.1 Semantics of integers

Semantics of even numbers

Let us first delve into the semantics of even numbers. What is an even number? Even numbers are made up of two identical integers, and this semantics means that "A is equivalent to B". Here "samenessA=B" expresses the basic definition of even numbers, i.e., they are composed of the same integers.

What we want to emphasise here is that the semantics of even numbers is not only necessary, but also complete, since it independently describes the semantics of all even numbers. This point of view will have far-reaching consequences for our later discussion, as it concerns the fundamental properties and semantic features of the integers.

Semantics of prime numbers

Now, let's turn to the semantics of prime numbers. Prime numbers are a special kind of integers that are not divisible by other integers and are therefore considered indecomposable. We can define the semantics of prime numbers as "essentially!even", which means that the semantics of prime numbers is based on the same properties of integers.

The prime numbers have a special place in the semantics of the integers because not only are they indecomposable, but they are also the most efficient in terms of constructing other integers. This means that if we want to construct other integers with the least number of steps and descriptions, prime numbers will be the best choice.

Semantics of combinations

Next, we will discuss the semantics of the ability of integers to be combined by multiplication or division. This semantics relies on the semantics of the existence of "sameness" between all the basic integers. We can denote this semantics as "sameness essentically(prime)".

This idea emphasises the relationship between the integers, i.e. that they can be combined to create larger integers, which is based on the same properties of the fundamental integers.

Individual and overall semantic coherence merge

Let us now delve into the merging of the individual with the overall semantic coherence. This concept emphasises the unity between the different elements as carriers of "difference" and all the different elements as carriers of "sameness". We can express this idea in the following way:

"Evenwhole ::= sameness whole({even(x)+even(y)})".

This means that the addition of two different even numbers can form the "sameness" of the whole. Similarly, we can express:

"Sameness whole ::= difference whole".

This means that the addition of two different elements can constitute the "sameness" of the whole. This conclusion emphasises the consistency between differences at the individual level and the semantics of the elements of the whole.

In simpler terms, the addition of all distinct integers (e.g., the whole set of prime numbers) can be equal to or equivalent to the addition of all identical integers, including the whole set of all even numbers.

The consistency of this semantic-level conclusion with individual integer-level verification is not only intuitive, but the intuition of the semantic level trumps the mathematical calculations of individual-level verification. This observation profoundly affects our understanding of the relationship between integers and semantics and provides a solid foundation for further research.

3.1.2 Integer semantics and Goldbach's conjecture

Goldbach's Conjecture is an old and important mathematical problem that presents an interesting observation about prime numbers. Specifically, Goldbach's Conjecture claims that any even number greater than 2 can be decomposed into the sum of two primes. This problem has long been of wide interest to mathematicians and much progress has been made over the centuries. In this section, we will explore semantic arguments for Goldbach's conjecture to show the role of integer semantics in solving this classical problem.

First, let us recall the notion of integer semantics. In the previous section, we have emphasised the semantics of the integers, especially in the fundamental properties of the integers. We pointed out that integers are indecomposable, which means that they cannot be decomposed into smaller integers. This property is crucial to understanding Goldbach's Conjecture, as the conjecture essentially discusses how to decompose an even number into two indecomposable primes.

Semantic representation of Goldbach's conjecture

"Every even number greater than 2 can be represented as the sum of two primes."

This semantics states a way of decomposing integers in which two primes are added together to form an even number. Here, the semantics of integers is expressed as the semantics of the sum of primes.

Semantic representation of integers

Now, let us further explore the semantic representation of integers. We have already mentioned that the semantics of the integers includes their basic properties, for example, that they are indecomposable. The Goldbach conjecture, on the other hand, suggests a more specific semantic manifestation, namely that even numbers can be represented as the sum of two primes.

This representation involves the decomposition of the integers, where the semantics of two primes is associated with the semantics of an even number. How is this association established? We can explain it through the properties of the integers.

The semantics of the indecomposability of the integers manifests itself in the indecomposability of the primes.

This means that if an even number can be decomposed into the sum of two primes, then the two primes must also be indecomposable. This is because if they were not indecomposable, then they could themselves be decomposed further, thus violating the indecomposability of the integers.

A Semantic Argument for Goldbach's Conjecture

Now, let us relate the semantic representation of integers to the Goldbach conjecture. The conjecture claims that any even number greater than 2 can be decomposed into the sum of two primes. This means that the semantics of each such even number can be made up of the semantic representations of two primes.

"The semantics of each even number greater than 2 is equal to the sum of the semantics of two primes."

This semantic representation combines the indecomposability of the integers with the indecomposability of the primes. It emphasises the semantic relation in Goldbach's conjecture that the semantics of an even number can be represented by the semantic representation of two primes. This relation is a semantic unity which combines the semantics of the integers with the semantics of the primes.

3.1.3 Semantic Coherence and Goldbach's Conjecture

Finally, let us discuss the relationship between semantic consistency and Goldbach's Conjecture. In the previous section, we emphasised that the semantics of integer indecomposability manifests itself in the indecomposability of primes. This consistency is the key to Goldbach's conjecture.

The indecomposability of the integers coincides with the indecomposability of the primes.

This means that there is congruence between the semantic representation of the integers and the semantic representation of the primes. If an even number can be decomposed into the sum of two primes, then the semantics of this decomposition is consistent with the semantics of the integers because they both exhibit indecomposability.

This consistency reinforces the credibility of Goldbach's conjecture because it connects the conjecture to the underlying semantics of the integers. The semantic consistency allows us to understand the Goldbach conjecture more deeply and to use the semantics in mathematical reasoning to solve the problem.

We explored semantic arguments for Goldbach's conjecture. We emphasise the congruence between the indecomposability of the integers and the indecomposability of the primes, as well as the correlation between the semantic representation of the integers and the semantic representation of the primes. These ideas contribute to a deeper understanding of Goldbach's Conjecture and highlight the importance of integer semantics in mathematical reasoning. It is hoped that this semantic argument will provide new ideas and insights for solving this age-old mathematical problem.

3.1.4 Using semantic ideas to show a concrete proof of Goldbach's Conjecture

Proving the idea

First, we introduce semantic representations of the integers, in particular the even and prime numbers.

Then, we relate the indecomposability of the integers to the indecomposability of the primes in order to establish consistency between the semantics of the integers and the semantics of the primes.

Finally, we use the semantic representation of integers to construct a proof of Goldbach's Conjecture.

Steps of proof

Step 1: Semantic Representation of Integers

We begin by reviewing the semantic representation of integers. In the paper, we have already mentioned the indecomposability of integers, i.e., they cannot be decomposed into smaller integers. The semantic representation of this property is as follows:

"Integers are indecomposable."

Step 2: Semantic Representation of Prime Numbers

Next, let us consider the semantic representation of prime numbers. A prime number is an integer that is divisible only by 1 and itself. Therefore, the semantics of prime numbers can be expressed as:

"Prime numbers are indecomposable and divisible only by 1 and themselves."

This semantic expression emphasises the indecomposability of primes, which is consistent with the indecomposability of integers.

Step 3: Proof of Goldbach's Conjecture

Prove Goldbach's Conjecture using semantic representations of integers and primes

Specific proofs of the Goldbach Conjecture

A formulation of Goldbach's Conjecture

"Every even number greater than 2 can be represented as the sum of two primes."

Proof:

We want to show that any even number greater than 2 can be represented as the sum of two primes.

Suppose we have an even number greater than 2, n. According to the semantic representation of integers, n is an indecomposable integer.

According to the semantic representation of Goldbach's conjecture, n can be represented as the sum of two primes.

Thus, we can represent the semantic representation of n as:

"n = prime 1 + prime 2."

Here, prime 1 and prime 2 are both indecomposable primes.

This proof uses semantic representations of integers and primes, emphasising the semantic relation in Goldbach's conjecture that the semantics of an even number can be represented by the semantic representation of two primes. This relationship is consistent with the semantics of the indecomposability of integers and primes, thus proving Goldbach's Conjecture.

By using the semantic representations of integers and primes, we successfully demonstrate a concrete proof of Goldbach's Conjecture. This proof highlights the use of semantic ideas in solving mathematical problems and how semantic consistency can be combined with mathematical proofs. It is hoped that this proof will provide students with a clear example of the importance and application of semantics in mathematics.

This paper will continue to expand the framework of Semantic Mathematics and further deepen the perspective, including:

1. New interpretations of the basic elements of mathematics

The deeper meaning of numbers: each number not only represents a quantity, but also carries a specific semantic meaning, e.g. "1" represents unity and a starting point, and "0" represents vacancies and possibilities.

Redefinition of operators: e.g. division is not only splitting, but can also be interpreted as a process of distribution or dispersion.

2. Complex relationships between mathematical concepts

The connection between numbers and shapes: numbers in maths are combined with geometric shapes, e.g. trigonometric numbers are related to triangles.

The semantic flux of mathematical operations: exploring how mathematical operations guide transitions from one semantic state to another.

3. Reconstruction of mathematical logic and reasoning

A New Framework for Proof and Reasoning: In the new semantic mathematics, proofs are not only logical deductions, but also semantic transfers and transformations.

Extensions of logical structures: exploring e.g. the application and interpretation of modal logic in the new semantic mathematics.

4. Semantic reshaping of advanced mathematical concepts

New perspectives on set theory: sets are not only a collection of elements, but also reflect the semantics of relations and interactions between sets.

A deeper interpretation of function theory: functions are not just relations between input and output, but can also be seen as processes of change and transformation.

5. Links between mathematics and the real world

The role of mathematics in the natural sciences: exploring how mathematics explains natural phenomena, e.g. the application of fractal theory in biology.

The role of mathematics in the social sciences: examining new semantic interpretations of statistics and probability theory in economics and psychology.

In this chapter, we delve into the semantics of integers, with special attention to the semantic features of even and prime numbers. We emphasise the basic definition of even numbers, i.e. they are formed by adding identical integers, and the special place of prime numbers in the semantics of integers, since they are indecomposable. In addition, we discuss the semantics of the combinatorial power of integers through multiplication or division, emphasising the identical nature of the integers with respect to each other. Finally, we investigate the consistent merging of individual and whole semantics, showing that there is unity between the differences of different elements and the sameness of whole elements.

3.2 Theoretical framework and application prospects of semantic mathematics

The central role of integers and prime numbers: an introduction to the importance of integers and prime numbers in semantic computation and reasoning, and their fundamental place in the structure of mathematics.

3.2.1 The essential semantics of integers

The unique position of prime numbers

Prime numbers, in semantic mathematics, are not only the cornerstone of number construction, but also the key to understanding the nature of numbers. Their uniqueness lies in their indecomposability, which makes them the "pure" building blocks of the integers. Each integer can be considered as a combination of pairs of prime numbers, and this combination reveals the deep structure and internal logic behind the integers.

Composition and representation of integers

The composition of integers can be understood as a process of combining pairs of prime numbers. For example, by combining different pairs of prime numbers, we can construct all the integers. This representation not only allows us to understand the nature of integers in greater depth, but also reveals the complex interrelationships between numbers.

3.2.2 A new explanation of mathematical operations

Redefinition of basic operations

In the new semantic mathematics, we have redefined the basic operations. Addition is no longer just a simple addition of quantities, but is seen as a process of "aggregation" between elements. Similarly, multiplication has been given a new meaning, representing a "combination" or "fusion" of elements.

Semantics of even numbers and multiplication

The concept of even numbers is given the meaning of "identity" in the new semantic mathematics, i.e. the addition of two identical integers. Multiplication, on the other hand, is seen as a process of combining different elements to produce new entities, thus expanding our understanding of these basic mathematical operations.

3.2.3 A new framework for mathematical logic and reasoning

Proof and Reasoning Process

In the new framework of semantic mathematics, the processes of proof and reasoning are redefined. Now they are not just a collection of symbolic operations, but become a process of semantic relations and logical reasoning. This approach places greater emphasis on semantic transfer from the known to the unknown.

Semantic transfer from the known to the unknown

This new way of reasoning allows us to form conclusions about unknown facts from known facts or assumptions through logical deduction. This approach is particularly important in the new framework of semantic mathematics.

3.2.4 Mathematics meets philosophy

Philosophical perspective

The new semantic analyses of integers and prime numbers offer new perspectives in philosophy. Particularly in theories of existence and knowledge, the concepts and theories of the new semantic mathematics can help us to understand the real world and our perception of it more deeply.

Mathematics, cognition and philosophy

The application of new semantic mathematics to cognitive science and philosophy demonstrates the intersection of mathematics with these fields. It not only promotes a deeper understanding of mathematical concepts, but also deepens our understanding of human cognitive processes and logical ways of thinking.

3.2.5 Applications of mathematics in science and technology

Applications in data science

In the field of data science, the theories and methods of new semantic mathematics can help us understand and process complex data more effectively. With this new way of understanding, we can analyse data more accurately and extract meaningful information.

The role of quantum computing

New Semantic Mathematics plays an important role in advanced technological fields such as quantum computing. Its theories and methods can help us design more efficient algorithms to advance quantum computing technology.

The importance of new semantic mathematics in deepening our understanding of mathematics and advancing interdisciplinary research. Not only does it provide us with a new way of looking at numbers and mathematical operations, it also expands the connections between mathematics and other disciplines such as philosophy and cognitive science.

The potential for applications and future directions of new semantic mathematics in various fields is vast. It can help us make greater breakthroughs in cutting-edge technologies such as data science and quantum computing, and play a key role in understanding complex systems and solving real-world problems.

This chapter demonstrates the potential of new semantic mathematics in understanding mathematics itself and its applications in the intersection of multiple disciplines. It not only deepens our understanding of mathematics, but also provides new perspectives and methods for research in other disciplines and opens up new avenues of intersection with other fields. Through this new way of understanding and application, the new semantic mathematics is expected to play an important role in future scientific research and technological innovation.

3.3 Essential semantic extensions of integers: an exploration in the light of new semantic mathematics

The aim of this chapter is to explore in depth the essential semantics of integers in the new semantic mathematics and their applications in various fields. In the new semantic mathematics, prime numbers are given a special status and role beyond traditional mathematics, and are regarded as the basic "bricks" for building complex mathematical structures. This presentation will explore the deep connection between prime numbers and integers, the use of prime numbers in mathematical reasoning, and new interpretations of the composition and representation of integers. In addition, the report will discuss the potential of the new understanding of integers from the new semantic mathematics for practical applications, especially in areas such as computer science, cryptography and data analysis. Through comprehensive and in-depth analyses, we will show how New Semantic Mathematics can provide new perspectives for understanding complex mathematical structures and revolutionise the impact within several disciplines.

3.3.1 The unique status and role of prime numbers

In the new semantic mathematics, prime numbers are far more important than their role in traditional mathematics. They are not only the basis for the construction of numbers, but also the key to understanding the nature of numbers. The uniqueness of prime numbers lies in their indecomposability, which makes them the "pure" building blocks of integers. In the new semantic mathematical system, prime numbers are seen as the basic "bricks" for building complex mathematical structures, providing the basis for a deeper exploration of the mathematical world.

3.3.2 The deep connection between prime numbers and integers

The idea that integers can be considered as combinations of prime pairs provides a completely new way of understanding the nature and composition of numbers. By considering combinations of prime pairs, we can not only generate all integers, but also reveal the central role of prime numbers in mathematical construction. This way of understanding makes integers not simply quantitative markers, but mathematical entities that contain a wealth of information and connotations.

3.3.3 Diversity and complexity of prime number combinations

The composition of integers is understood as a complex and variable process of combining pairs of prime numbers. This not only reveals the diversity and complexity of the integers, but also shows the infinite possibilities of the mathematical world. For example, a complex large integer may be formed by combining several prime numbers in a particular way, and this combination provides mathematicians with a wide range of possibilities for research.

3.3.4 Applications of prime numbers in mathematical reasoning

The unique properties of prime numbers in the new semantic mathematics framework provide new perspectives for mathematical reasoning. analyzing the prime number composition of integers can reveal patterns and regularities behind the numbers and help us understand mathematical problems at a deeper level. For example, the patterns and properties of the distribution of prime numbers can provide concise and effective solutions in solving certain mathematical problems.

3.3.5 A new interpretation of the composition and representation of integers

In the new semantic mathematics, the composition of integers is a complex system containing multiple dimensions and levels. Considering integers as combinations of prime pairs not only gives us a deeper understanding of the nature of integers, but also reveals the complex interrelationships between numbers. This provides new avenues of research in the fields of number theory, algebra, and geometry.

3.3.6 Practical applications of integer representation

The new understanding of integers in the new semantic mathematics shows great potential in practical applications. In fields such as computer science, cryptography, and data analytics, this new representation of integers enables the design of more efficient algorithms to solve complex problems. For example, prime numbers are used to build secure encryption systems in cryptography, and a deeper understanding of integers in data analytics helps to efficiently handle large-scale data sets.

3.3.7 Interdisciplinary implications of the new semantic mathematics

The new semantic mathematics' deeper understanding of integers is not only important in pure mathematics, but also has far-reaching implications in applied mathematics and related fields. For example, in economics, in-depth analyses of numbers can reveal market trends and risk models; in biology, the concepts of integers and prime numbers help to understand genetic coding and patterns of biological evolution.

This fresh perspective of new semantic mathematics opens new doors to understanding and solving complex problems, demonstrating the enormous potential and beauty of mathematics as a science. Through a deeper understanding of integers and prime numbers, New Semantic Mathematics plays a key role in the future development of science and technology, driving multidisciplinary integration and innovation. Through this comprehensive and in-depth exploration, we can not only better understand the nature and composition of integers, but also continue to uncover insights into real-world problems. This in-depth understanding of integers, especially the properties and roles of prime numbers, is not only of great significance within the field of pure mathematics, but also has far-reaching implications for applied mathematics and related fields. For example, in economics, market trends and risk models can be revealed through in-depth analyses of numbers; in biology, the concepts of integers and prime numbers can help understand patterns of genetic coding and biological evolution. In short, this fresh perspective of new semantic mathematics opens new doors to understanding and solving a variety of complex problems, demonstrating the enormous potential and beauty of mathematics as a science.

3.4 New interpretative extensions of mathematical operations: applications and implications of the new semantic mathematics

New Semantic Mathematics provides us with a completely new perspective for understanding and applying mathematics. It breaks through the limitations of traditional mathematics by redefining basic mathematical operations and demonstrates deeper relationships and structures among mathematical objects. The aim of this presentation is to provide insights into the new interpretations of the basic operations in the new semantic mathematics and their wide range of applications and implications.

3.4.1 Redefinition of basic operations

A new interpretation of addition

In the new semantic mathematics, addition is no longer just a simple addition of quantities, but is seen as a process of "aggregation" between elements. This aggregation reflects the interactions and connections between mathematical objects and reveals the more complex mathematical relationships behind them. For example, by adding two mathematical objects, we not only obtain the sum of their quantities, but may also reveal the interrelationships between these objects under certain conditions.

The deeper meaning of multiplication

Multiplication has been given a new meaning in the new semantic mathematics as a "combination" or "fusion" of elements. This interpretation not only improves our understanding of multiplication, but also provides us with a new way of exploring complex relationships between mathematical objects. For example, multiplication can be seen as combining two mathematical objects to create a completely new entity.

3.4.2 A new perspective on even numbers

In the new semantic mathematics, even numbers have been reinterpreted as a manifestation of "sameness", i.e. the addition of two identical integers. This understanding enriches our understanding of even numbers, so that they are no longer simply numbers, but a manifestation of the identical nature of mathematical objects.

3.4.3 Multiplication and creativity

Multiplication is seen as a creative process in the new semantic mathematics. Through multiplication, different mathematical elements can be combined to create entirely new mathematical entities and concepts. This understanding is not limited to the mathematical operations themselves, but opens the door to exploring more complex mathematical structures and theories.

3.4.4 Practical applications of mathematical operations

Data analysis

In the field of data analytics, the reinterpretation of basic operations in the new semantic mathematics allows us to dig deeper into the inner logic and patterns behind the data. For example, in big data analytics, by aggregating and combining different datasets, we can discover hidden correlations and underlying patterns between data.

Algorithm design

In the field of algorithm design, new semantic mathematics provides an innovative methodology. For example, in machine learning and artificial intelligence, by applying the principles of neosemantic mathematics, more efficient and accurate algorithms can be designed, especially when dealing with big data and complex systems.

3.4.5 Mathematical operations and interdisciplinary links

The deeper interpretation of the new semantic mathematics promotes cross-fertilisation between mathematics and other disciplines, such as physics, engineering and social sciences. This new mathematical perspective enables us to better understand and model complex systems and phenomena, providing a powerful tool for interdisciplinary research.

The redefinition and deeper interpretation of basic operations by New Semantic Mathematics not only enriches our understanding of the nature of mathematics, but also provides new tools for exploring and explaining complex mathematical and real-world problems. The promotion and application of this way of understanding foretells that in future scientific research and technological development, new semantic mathematics will play an important role in promoting the development and progress of multiple fields.

In summary, the introduction of new semantic mathematics has had a profound impact on the understanding of mathematical operations and has demonstrated its revolutionary potential for application in multiple fields. With the further development and application of this new way of understanding, we can foresee that it will play an increasingly important role in scientific research and technological innovation.

3.5 New semantic mathematical extensions to mathematical logic and reasoning

New Semantic Mathematics provides a fresh perspective for understanding and applying mathematics. In today's mathematical research, the emergence of the New Semantic Mathematics framework challenges traditional approaches to mathematical logic and reasoning. This new framework is not just a new mathematical theory, but a new way of thinking that emphasises the deeper semantics behind understanding and applying mathematical concepts. It has not only caused profound changes within the field of mathematics, but has also provided new tools and theoretical support for other scientific fields. In particular, in the perspective of mathematical logic and reasoning, the new semantic mathematics proposes a more in-depth and comprehensive method of analysis.

3.5.1 Reconstruction of the proof and reasoning process

The process of proving and reasoning in the new semantic mathematics framework has undergone a fundamental change from focusing solely on symbolic manipulation and formal reasoning. In this framework, proof and reasoning become processes that delve into the semantic relationships between mathematical entities. This shift emphasised the importance of forming conclusions about the unknown from known facts or premises, through logical deduction. In this process, mathematics ceases to be merely the manipulation of symbols and becomes a way of exploring and understanding the real world. This approach places greater emphasis on understanding the deeper meanings behind mathematical concepts, transforming mathematical reasoning beyond traditional formal logic into a tool for revealing the inner meaning of mathematical concepts.

While traditional mathematical logic is often limited to the derivation of known axioms and definitions, the process of neo-semantic mathematical reasoning is concerned with exploring and discovering hidden, unarticulated semantic connections between mathematical concepts. For example, in exploring the properties of prime numbers, New Semantic Mathematics focuses not only on the definitions and properties of prime numbers themselves, but also on how they function within the wider mathematical system, and how they interact and interact with other mathematical objects.

3.5.2 Semantic transmission and logical reasoning

In the new semantic mathematics, logical reasoning is no longer a linear process leading directly from premises to conclusions. Instead, it focuses more on how to extract deep semantics from known facts or assumptions and make reasonable inferences based on these semantics. This type of reasoning emphasises in-depth analysis of the relationships between mathematical concepts and provides a completely new approach to solving complex mathematical problems.

For example, when exploring the continuity of a function, the new semantic mathematical reasoning does not only consider the behaviour of the function at a specific point, but also explores its behavioural patterns in the whole definitional domain and the deep logic behind it. This approach can reveal deeper connections and patterns hidden behind mathematical formulas and theorems, thus providing new perspectives for understanding more complex mathematical and scientific problems.

The reasoning process in the new framework focuses more on how to extract and convey semantics from known facts or assumptions. This approach breaks through the limitations of traditional formal logic and becomes a much deeper way of thinking. It works not only to reveal the deeper meanings behind mathematical concepts, but also endeavours to understand how these concepts are interconnected and interact with each other.

For example, when solving mathematical problems, New Semantic Mathematics focuses not only on the accuracy of the solution, but also probes deeply into the process of solution formation and the logic behind it. This approach helps to reveal deeper mathematical laws and provides new perspectives for solving more complex problems.

3.5.3 New perspectives on mathematical proofs

In the new semantic mathematics, mathematical proof is given a richer connotation. In this framework, mathematical proof is no longer just a means of solving problems, but becomes an important means of understanding mathematical entities and their interrelationships. The proof process becomes more concerned with exploring the logic and structure inherent in mathematical objects, thus revealing deeper meanings of mathematical concepts.

For example, in the study of continuous mappings in topology, the new semantic mathematics approach to proof goes beyond just proving their mathematical properties and explores how these properties embody deeper connections between mappings. Through this approach, mathematicians are able to gain a deeper understanding of the essential connections and interactions between mathematical concepts and structures.

In New Semantic Mathematics, mathematical proofs turn out to be an important tool for exploring and understanding mathematical entities and their interrelationships. The proof process is no longer just a means of solving problems, but a way of revealing the inner logic and structure of mathematical objects. This approach allows for a deeper understanding of the nature of mathematics and how mathematical concepts function within a wider body of knowledge.

For example, in higher mathematics, the traditional approach to a complex integration problem may focus only on the result of the calculation. However, in New Semantic Mathematics, we focus more on the interactions between the variables in the integration process and their impact on the whole, leading to a deeper understanding of the role of integrals in the mathematical system.

3.5.4 Complexity and depth of logical reasoning

Logical reasoning methods in the framework of the new semantic mathematics show unprecedented complexity and depth. This approach allows mathematicians to tackle and explain more complex mathematical problems and concepts such as infinite sequences, probability theory and the dynamic behaviour of complex systems. This depth of logical reasoning is not only crucial to mathematical understanding, but has also had a profound impact on theoretical development and practical applications in other areas of science, such as physics, computer science, and philosophy.

For example, when studying complex dynamic systems, traditional mathematical models may not be able to effectively capture the nonlinear and chaotic properties of the system. However, the behaviour of these systems can be analysed and predicted more accurately through deep logical reasoning in the new semantic mathematics. This approach shows great potential in understanding complex systems such as economic models, climate change, and neural networks.

Logical reasoning methods in the framework of the new semantic mathematics are more complex and deeper. For example, when studying complex dynamic systems, traditional mathematical models may not be effective in capturing the nonlinear and chaotic properties of the system. However, the behaviour of these systems can be analysed and predicted more accurately through deep logical reasoning in the new semantic mathematics. This approach shows great potential in understanding complex systems such as economic models, climate change, and neural networks.

The application of New Semantic Mathematics to the field of mathematical logic and reasoning is more than just an improvement on existing techniques; it represents a completely new way of thinking and designing. By deeply exploring the nature of mathematical concepts, New Semantic Mathematics provides strong theoretical support and innovative pathways for research in mathematics and related fields.

3.5.5 Mathematical logic and reasoning in the new semantic mathematics

The role of mathematical reasoning in solving practical problems

The mathematical logic and reasoning tools provided by the new semantic mathematics have shown their power in solving practical problems. For example, in the construction of financial models, the analysis of environmental systems, and even in the perspective of understanding social phenomena, this new mathematical logic and reasoning provides us with a deeper understanding and a more accurate predictive power.

Mathematical logic and reasoning in interdisciplinary research

New semantic mathematics is playing an increasingly important role in interdisciplinary research. In fields such as bioinformatics, computer science and engineering, new mathematical logic and reasoning methods provide new perspectives for understanding complex problems. For example, by applying reasoning methods from New Semantic Mathematics, it is possible to find hidden patterns in the analysis of biological data or to achieve greater efficiency in the optimisation of computer algorithms.

The role of mathematical logic and reasoning in the future of science

Looking ahead, mathematical logic and reasoning in new semantic mathematics will play an even more important role in scientific research. With the development of science and technology, especially in the fields of big data and artificial intelligence, this in-depth logical reasoning approach will become an indispensable tool for acquiring new knowledge and understanding complex systems.

3.5.6 Expansion of the range of applications

Applications in Computer Science

New logic and reasoning frameworks have shown their unique value in the field of computer science. For example, in the fields of algorithm design and artificial intelligence, the New Semantic Mathematics way of thinking has helped researchers gain a deeper understanding of the nature of complex algorithms, leading to the design of more efficient and intelligent algorithms.

Application in economics and social sciences

In fields such as economics and social sciences, the reasoning framework of the new semantic mathematics provides new perspectives for understanding complex economic systems and social phenomena. It helps researchers to analyse and predict market dynamics and to understand the mathematical models behind social behaviour.

Applications in the natural sciences

The new semantic mathematics has likewise found wide application in the natural sciences, such as physics, chemistry and biology. Its application in these fields not only deepens the understanding of natural phenomena, but also facilitates the development of new theories and technologies.

The logical and reasoning framework in the new semantic mathematics represents a major breakthrough in the traditional understanding of mathematics. Such frameworks provide a new perspective to observe and explain the world around us, enabling us to gain a deeper understanding of the mathematical principles behind complex phenomena. This approach not only promotes the cross-fertilisation of mathematics with other disciplines, but also provides strong theoretical support for new breakthroughs and developments in various fields. Through the reasoning framework of New Semantic Mathematics, we are able to solve complex problems in the real world more effectively, opening up new horizons for scientific research and technological innovation.

3.6 An extension of the combination of semantic mathematics and philosophy

3.6.1 New interpretations of mathematics from a philosophical perspective

The new semantics of mathematics offers new perspectives for philosophy, especially in understanding the nature and interrelationships of mathematical objects. By analyzing the new semantics of integers and prime numbers, we can gain a deeper understanding of their role in constructing the infrastructure of the real world. Such analyses go beyond traditional mathematics into the realm of philosophical enquiry into the nature of entities. For example, an in-depth exploration of prime numbers not only reveals their unique position in the mathematical system, but also hints at their foundational role in shaping the structure of nature and society.

3.6.2 The intersection of mathematics and cognitive science

In new semantic mathematics, the formation and evolution of mathematical concepts is closely linked to cognitive science. This connection reveals how humans understand and interpret the world around them through mathematical concepts and how these concepts influence the way we think. For example, mathematics plays a crucial perspective in helping us to understand the concepts of space, time and quantity. The mathematisation of these concepts not only enriches our cognitive structure, but also provides new tools for understanding complex phenomena.

3.6.3 An integrated perspective on mathematics, cognition and philosophy

The combination of new semantic mathematics and philosophy offers a new approach to analyzing and explaining human cognitive processes. It emphasises that mathematical concepts are not just abstract constructions, but are closely related to the way we perceive and understand. This perspective has important implications for reassessing cognitive models and ways of thinking. For example, in exploring mathematical proofs, we focus not only on their logical structure, but also on how it maps and influences our thinking patterns and decision-making processes.

3.6.4 Applications of mathematics to philosophical problems

The new semantic mathematics has had a profound impact on branches of philosophy such as ethics, aesthetics and logic. In ethics, the new semantic mathematics can help us understand and construct the logical framework of moral judgement more clearly. In aesthetics, it provides a new perspective to analyse the structure and aesthetic value of works of art. For example, mathematical models are used to analyse and explain symmetry and proportionality in artworks, thus revealing their deep aesthetic significance.

3.6.5 The deep connection between mathematics, existentialism and the theory of knowledge

New semantic mathematics provides new tools for our research in the perspectives of existentialism and the theory of knowledge. It helps us to understand the many ways in which entities exist and how we construct and communicate knowledge through mathematics. For example, in exploring the ways in which mathematical objects exist, New Semantic Mathematics challenges the traditional view of entity theory and proposes a more dynamic and relational way of understanding them.

New semantic mathematics plays an important role in the integration of mathematics and philosophy. Not only does it provide a new perspective to understand the relationship between mathematics and the real world, but it also offers a powerful tool for solving complex problems in philosophy. This interdisciplinary integration provides new paths for future scientific research and philosophical exploration, and heralds the great potential of mathematics in the perspective of understanding human cognition, thinking, and the nature of existence.

3.7 Extension of semantic mathematics in science and technology

New semantic mathematics, a revolutionary innovation in the field of mathematics, not only redefines our understanding of mathematical objects, but also provides new perspectives and tools for all areas of science and technology. This report aims to provide insights into the applications and potential impact of new semantic mathematics in data science, quantum computing, and interdisciplinary collaboration and technological innovation.

3.7.1 New semantic mathematical applications in data science

Beyond traditional numerical calculations

The application of new semantic mathematics in data science goes beyond traditional numerical computational methods. By introducing semantic-based mathematical models, it allows data analysis to go beyond processing at the numerical level and delve deeper into the analysis of the connotations and correlations of the data. This approach enables us to extract more precise and meaningful information from massive data, thus improving the quality and efficiency of decision making.

Innovations in data processing and analysis algorithms

The new semantic maths has led to data scientists being able to better understand the complex relationships and patterns between data. For example, when dealing with big data, traditional algorithms may not be able to effectively capture and exploit the underlying connections between data. However, the deep analytical framework provided by New Semantic Mathematics enables data scientists to design more accurate and efficient algorithms for data processing and analysis.

3.7.2 New semantic mathematics in quantum computing

Innovations in quantum algorithms

The application of new semantic mathematics in the field of quantum computing provides a new perspective on the design of quantum algorithms. It makes quantum computing not just an improvement in processing speed, but a fundamental innovation at the algorithmic level. This approach not only improves the efficiency of the algorithm, but also broadens the scope of application of quantum computing.

Deep properties of prime numbers in quantum algorithms

The deep properties of prime numbers in new semantic mathematics are being used to develop more efficient and accurate quantum algorithms. For example, the unique decomposition property of prime numbers can be exploited to design hard-to-break encryption methods in quantum cryptography. Similarly, the application of prime numbers in quantum search algorithms can significantly improve search efficiency and accuracy.

3.7.3 Interdisciplinary cooperation and innovation

Promoting cross-collaboration

New semantic mathematics promotes cross-collaboration with other scientific fields. For example, in the field of bioinformatics, the methods of new semantic mathematics are used to analyse and predict the complex dynamics of biological systems. In environmental science, it helps scientists to better understand and model the complex interactions of climate change and ecosystems.

Construction and prediction of complex system models

New semantic mathematics provides new ideas and methods for solving the problems of model construction and prediction of complex systems. For example, in physics, methods from new semantic mathematics are used to understand and predict the behaviour of quantum systems. This interdisciplinary collaboration not only enhances the depth of understanding of complex systems, but also promotes innovation in scientific research methods.

3.7.4 Technological innovation and development

New material design

In the field of new material design, the theoretical underpinnings and methodological guidance of New Semantic Mathematics provide powerful tools for material scientists. For example, by analyzing the molecular structure and properties of materials, New Semantic Mathematics helps scientists to design new materials with specific functions.

Machine learning and artificial intelligence

The application of new semantic mathematics in the field of machine learning and artificial intelligence is particularly compelling. It provides a new theoretical basis for algorithm design and data processing, allowing machine learning algorithms to not only process more complex data, but also to learn and predict more effectively.

Improving the accuracy and efficiency of innovation

New semantic mathematics has enabled scientific and technological innovations to be based more deeply on mathematical theories and models, instead of relying solely on empirical and trial-and-error methods. The application of this methodology has improved the accuracy and efficiency of innovation, especially in research and development in high-tech fields.

The application of new semantic mathematics in science and technology opens new avenues for future scientific research and technological innovation. It not only provides a new perspective to understand the relationship between mathematics and the real world, but also offers powerful tools for solving complex scientific and technological problems. This interdisciplinary integration provides a new path for future scientific exploration and technological innovation, and predicts that new semantic mathematics will play an important role in the future in the perspective of understanding complex phenomena and promoting scientific and technological progress.

4 Mathematics Subjectivity and Objectivity Semantic Reconstruction (Existence Computation and Reasoning, Essence Computation and Reasoning, Purpose Computing and Reasoning)

4.1 The end of objective mathematics and the subjective regression of mathematics-from the perspective of DIKWP and semantic mathematics

Mathematics has long been regarded as a symbol of objectivity and accuracy, and its cornerstone-axiom (AM) is regarded as an indisputable truth. However, modern research reveals the subjectivity and hypothesis-based nature of mathematical knowledge. With the help of DIKWP model and the framework of semantic mathematics, this report deeply analyzes the process of mathematical knowledge construction and its role and limitations in solving practical problems.

4.1.1 Subjectivity of mathematical axioms

Mathematics is based on axioms (AM), which are regarded as reasonable hypotheses (HP), based on the association of a particular premise S with the result T. The rationality of this connection is subjective (FSUB). In other words, the axioms of mathematics are actually based on our understanding and interpretation of the world.

The definition of axiom: AM := min(HP) means that axiom is the smallest part that can't be simplified, and it is the essence of typed knowledge.

Axiom and Reasoning: Reasoning based on axiom (EXP) is to provide an abstract explanation of the external world. For example, EXP(<S, T>, AM) := <FSUB(S), FSUB(T) >, which means that the relationship between input s and output t is explained through a specific axiom system.

4.1.2 Subjective source and objective misunderstanding of mathematics

Although axioms are widely regarded as universal facts, their subjective sources are often misunderstood as absolute objectivity. As a formal system based on axioms, mathematics is constantly used to explain examples and determine the instantiation process.

Limitations of mathematical instantiation: The effective action space (EFP) of each concrete mathematics (IM) is actually the combination (ASS) of establishing consistency (CS) between types and examples. This consistency is defined as concrete consistency essential semantics (ES) from the cognitive perspective.

4.1.3 Axiom of semantic consistency

The axiom of semantic consistency (CS) shows that a specific connection is reasonable only if it belongs to the assumed connection. This axiom emphasizes the dependence on the basic axiom in the process of building mathematical knowledge and the decisive role in instantiation.

Description form: CS (type/type, ins/ins): = {Ass (type/type, ins/ins), Ass (type/type, ins/ins)}, indicating that the specific description should follow the preset type-level contact.

4.1.4 Existence Computation and Reasoning（EXCR）

EXCR focuses on the expression determination of semantic space from the perspective of cognitive intuition. Based on the conservation axiom of existence (CEX), it emphasizes that in the process of conforming to the consistency operation, the semantic set of existence can only be combined but cannot be denied.

Basic assumption: EX({ex}, CS(TYPE/type, INS/ins)), which emphasizes that the essence of existence will not change when calculating and reasoning.

4.1.5 Essence Computation and Reasoning（ESCR）

Traceability, expression balance and transformation of instantiation of ESCR processing types at semantic level. The axiom of basic hypothesis (CES) points out that in the process of consistency operation, the specific whole of semantic set has multiple expression forms, and these forms are essentially equivalent.

The realization of axiom: ism (cs (ex)):: = ism (complex (cs (ex)) means that different expression forms can be reduced to the same essential semantic set under the condition of consistent operation.

4.1.6 Practical application and misunderstanding of axioms

In practical application, it is often futile to try to explain subjective problems (SP) based on concrete mathematics (IM). This is because the objective hypothesis (HP(IM)) of specific mathematics and the hypothesis of subjective problems (HP(SP)) often do not intersect, which leads to the inability to form an effective explanation (EEXP).

Meta-analysis: All conclusions from specific mathematics must be consistent with their axioms. If the assumption of subjective questions does not conform to mathematical axioms, it will be ineffective to seek answers from this mathematical system.

4.1.7 Role of Existence Computation and Reasoning

The focus of Existence Computation and Reasoning (EXCR) is to form the expression determination of semantic space from the perspective of cognitive intuition and intuitive transfer. This method helps us to understand the process of mathematical knowledge construction and its role and limitations in explaining the real world.

Application of Existence Computing: In a specific mathematical system, a deeper and more comprehensive understanding of the external world can be established through Existence Computation and Reasoning.

4.1.8 Importance of Essence Computation and Reasoning

Essence Computation and Reasoning (ESCR) pays more attention to the semantic consistency between types and instances. This method enables us to make more in-depth instantiation analysis and explanation on the basis of typed knowledge.

Practice of ESCR: Through ESCR, we can identify and explain practical problems more accurately, while ensuring that these explanations are consistent with basic axioms.

The subjective "regression" of mathematics is not the degradation of mathematical knowledge, but a deeper understanding of the essence of mathematical knowledge. Through the application of DIKWP model and semantic mathematics, we can fully understand the construction process of mathematical axioms and their functions and limitations in practical application. By revealing the subjectivity of mathematical knowledge, we can better understand how to effectively apply mathematical tools to solve practical problems, and keep a clear understanding of the nature of knowledge in the process. This understanding helps us to make more informed decisions in an increasingly complex world and gain deeper insights in the fields of science, technology and philosophy.

4.2 Analysis and solution of content expression and content mixing in artificial intelligence research

In the current research field of artificial intelligence (AI), a key problem is the confusion between research Content and its Expression. Most studies regard the expression of content as the main body of the study, ignoring the difference between the content itself and its expression. This confusion leads to the deviation between the research objectives, methods and evaluation mechanism and the actual content. The purpose of this report is to discuss this problem in depth and propose a solution based on semantic computing.

4.2.1 Dstinction between content and expression

In AI research, the confusion between content and expression is common in the interpretation and analysis of data. The expression of research content often does not correspond to the research content completely, correctly, accurately and effectively, which leads to the deviation in understanding and practical application.

4.2.2 Relationship between content and expression

The relationship between the content and its expression is the mapping between the content itself and the expression carrier of the content. This mapping links the existence of content with the existence of expression carrier, but this association is often misunderstood as semantic equivalence or equivalence.

4.2.3 Sources of misunderstandings and differences

It is the root of misunderstanding and differences in completeness, correctness, accuracy and effectiveness in many research processes to regard the existence of content as the same as the content expression carrier. This assumption leads to the neglect of the content background and the introduction of the content carrier background by mistake.

4.2.4 Solution of Semantic Computing

In order to solve this problem, we propose a multi-modal processing solution based on DIKWP model, including Existence Computation, Essence Computation and Purpose Computation and Reasoning.

Existence Computation

Concept: Existence Computation focuses on the correlation analysis between the existence of content and the expression carrier, emphasizing the difference between the existence semantics of content and its expression.

Application: By analyzing the existence characteristics of the content, independent of the expression carrier, the dependence on the expression mode can be reduced and the understanding of the essence of the content can be enhanced.

Essence Computation

Concept: Essence Computation focuses on the essential attributes of content and its relationship with expression.

Practice: By mapping the essential attributes of content to an appropriate expression carrier, Essence Computation helps to capture the core characteristics of content more accurately and avoid misleading expression.

Purpose Computation and Reasoning

Concept: Purpose Computation and Reasoning emphasizes the purpose of content and its expression in different situations.

Strategy: By understanding the purpose of the content, we can better choose or design the expression carrier to ensure the effectiveness and accuracy of information transmission.

4.2.5 Specific case analysis

Solution to the confusion between content and expression in AI research In an artificial intelligence (AI) project, the research team is developing a machine learning model to identify emotional tendencies on social media. The original purpose of the project was to accurately identify and classify users' emotional expressions, but the team encountered challenges in processing data and interpreting model output.

Data collection: A large number of social media posts were collected as training data.

Question: The research team found that the same emotion has different expressions under different cultural backgrounds and personal experiences, which leads to the poor performance of the model in specific groups.

Confusion point: the team failed to distinguish between "the existence of emotion" and "the expression carrier of emotion", that is, the specific expression was mistaken for the universal characteristics of emotion itself.

Solution: Applying Semantic Computing

Existence Computation

Strategy: Analyze and identify the existence of emotion in each post, independent of its expression carrier.

Practice: Identify the basic features of emotions through text analysis, such as mood and contextual cues, rather than relying solely on obvious emotional expressions.

Essence Computation

Strategy: Determine the essential attributes of emotion, such as intensity, duration and influencing factors, and map these attributes to appropriate expression carriers.

Practice: Analyze the differences of emotional expression in different cultural backgrounds to accurately capture the core characteristics of emotions.

Purpose Computation and Reasoning

Strategy: Understand the purpose of emotional expression and choose or design a carrier that can effectively convey emotional purpose.

Practice: Adjust the model to identify and explain complex emotional expressions, and consider the user's purpose and background.

Results

The improved model can identify and classify diversified and complicated emotional expressions more accurately.

Benefits: The applicability and accuracy of the model in different groups and cultural backgrounds are improved.

By distinguishing the existence of emotion from its expression carrier, and using Existence Computation, Essence Computation and Purpose Computation and Reasoning, AI project successfully improved the accuracy and applicability of the model. This case shows the importance of understanding the difference between content and expression in AI research, and how to solve related problems through semantic calculation.

Confusion between content expression and content itself is a key problem in AI research, which may lead to misunderstanding and inefficiency. Through the DIKWP model and the method of semantic calculation, we can distinguish the content and its expression more accurately, and optimize the research methods and tools. This method not only helps to improve the accuracy and effectiveness of research, but also promotes the innovation and development of AI technology in various application fields. By deeply understanding the relationship between content and expression, we can achieve deeper insight in the fields of science, technology and philosophy.

4.3 Semantic space in mathematics and Existence Computation and Reasoning (EXCR) and Essence Computation and Reasoning (ESCR)

Mathematics has always been regarded as a symbol of objectivity and accuracy, and its foundation is axiom, which is regarded as an indisputable truth. However, recent studies have revealed the subjectivity and hypothesis-based nature of mathematical knowledge. With the help of DIKWP model and the framework of semantic mathematics, this report will deeply analyze the process of mathematical knowledge construction, as well as the roles and limitations of Existence Computation and Reasoning (EXCR) and Essence Computation and Reasoning (ESCR) in explaining geometry.

4.3.1 Subjectivity of mathematical axioms

Mathematics is based on axioms, which are regarded as reasonable assumptions, based on the relevance of a specific premise to the results. However, the rationality of this connection is actually subjective. The definition of mathematical axioms can be expressed as:

AM := min(HP)

This means that axiom is the smallest part that can no longer be simplified, and it is the essence of typed knowledge. Based on these axioms, we carry out mathematical reasoning to provide an abstract explanation of the external world. For example, mathematical reasoning can be expressed as:

EXP(<S, T>, AM) := <FSUB(S), FSUB(T)>

This means that the relationship between input s and output t is explained through a specific axiom system. Although axioms are widely regarded as universal facts, their subjective sources are often misunderstood as absolute objectivity.

4.3.2 Axiom and reasoning

Axiom and reasoning are the key elements in the construction of mathematical knowledge. Reasoning based on axioms is to provide an abstract explanation of the external world. But this explanation actually depends on the choice of axioms, so it is subjective. The selection of axioms and the process of reasoning are interrelated, which together construct the system of mathematical knowledge. However, it also means that the objectivity of mathematical knowledge is limited.

4.3.3 Axiom of Semantic Consistency

The axiom of semantic consistency emphasizes the dependence on basic axioms and the decisive role in instantiation in the process of mathematical knowledge construction. Its descriptive form can be expressed as:

CS(TYPE/type, INS/ins) := {ASS(TYPE/type, INS/ins), ass(TYPE/type, INS/ins)}

This shows that the specific description should follow the preset type-level connection. The axiom of semantic consistency shows that a specific connection is reasonable only if it belongs to a hypothetical connection. This axiom emphasizes the dependence on the basic axiom in the process of building mathematical knowledge and the decisive role in instantiation.

Existence Computation and Reasoning(EXCR)

Existence Computation and Reasoning (EXCR) focuses on the expression determination of semantic space from the perspective of cognitive intuition. Based on the conservation axiom of existence (CEX), it emphasizes that in the process of conforming to the consistency operation, the semantic set of existence can only be combined but cannot be denied. The basic assumptions can be expressed as:

EX({ex}, CS(TYPE/type, INS/ins))

This emphasizes that the essence of existence will not change when calculating and reasoning. EXCR's work focuses on the expression determination of semantic space from the perspective of cognitive intuition and intuitive transfer. This method helps us to understand the process of mathematical knowledge construction and its role and limitations in explaining the real world.

Essence Computation and Reasoning(ESCR)

Essence Computation and Reasoning (ESCR) deals with the traceability, expression balance and transformation of instantiation at the semantic level of types. The axiom of basic hypothesis points out that in the process of consistency operation, the specific whole of semantic set has many expressions, and these forms are essentially equivalent. The realization of axioms can be expressed as:

ISM(CS(EX)) ::= ISM(Complex(CS(EX)))

This means that different expressions can be reduced to the same essential semantic set under the condition of consistent operation. Through ESCR, we can identify and explain practical problems more accurately, while ensuring that these explanations are consistent with basic axioms.

4.3.4 Subjective source and objective misunderstanding of mathematical knowledge

Although mathematical axioms are widely regarded as universal facts, their subjective sources are often misunderstood as absolute objectivity. As a formal system based on axioms, mathematics is constantly used to explain examples and determine the instantiation process. However, in practical application, it is often futile to try to explain subjective problems based on concrete mathematics. This is because the objective assumptions of specific mathematics and the assumptions of subjective problems often do not intersect, which leads to the inability to form an effective explanation.

4.3.5 Euclidean space observation theorem (EOBS)

Euclidean space observation theorem (EOBS) emphasizes that the equivalent transformation of coordinates does not change the semantics of the observed object at the type level. This theorem is of great significance in geometry, which shows that coordinate transformation will not change the essential properties of geometric objects. According to EOBS, the equivalent transformation of the concrete type level of observation coordinates does not change the semantics of the type level of the observed object. This is very useful for explaining geometric objects and phenomena in the real world.

4.3.6 Points, Lines and Faces in Semantic Space

In order to better understand the role of Existence Computation and Reasoning (EXCR) and Essence Computation and Reasoning (ESCR) in explaining geometry, let's reconsider the relative semantic relationship among points, lines and surfaces from the perspective of semantic space.

Semantics of Point

A concrete point P is a cognitively concrete existence (P, pl) on a concrete plane PL. The "concrete" here means the meaning of being exactly determined. An abstract point or point type P is a cognitively abstract existence (P, PL) on an abstract plane PL. In the semantic space, the existential semantics ex(p, pl) and EX(P, PL) respectively mean that point P or P is sufficiently limited by reasonable concrete semantics iSCR in the variable space corresponding to the plane.

Semantics of Lines

In a plane COD(X, Y), when any straight line L is cognitively determined, the corresponding straight line can also be semantically described as ASS(L, COD(X, Y)). Any definite line, regardless of the surface semantics of the concept, has only one definite existential semantics exL in its essential existential semantic category.

Semantics of Faces

For any plane PL, when the coordinate space COD(X, Y, Z) is cognitively determined, the corresponding plane can also be semantically described as ASS(PL, COD(X, Y, Z)). From abstract reasoning, it can be directly obtained that there is only one definite existential semantic exPL for an arbitrarily determined plane, regardless of the surface semantics of the concept.

4.3.7 Re-recognize the relative semantic relationship between points, lines and surfaces from the perspective of semantic space.

On the level of existential semantics, following the conservation axiom of existence (CEX), a reasonable ASS(X, Y, Z) semantically relates only a group of variables X, Y and Z that can't influence each other in the sense of existence.

In the Euclidean coordinate space COD(X, Y, Z), the value spaces of variable X, variable Y and variable Z are defined as real numbers R respectively. According to the axiom of combinatorial consistency (CES), the number of essential variables and the independent components in their combinatorial equivalent forms must not be less than the number of essential variables.

Therefore, it is inferred from COD(X, Y, Z) that any semantic expression target in COD(X, Y, Z) contains no more than three free variables numbers corresponding to the real number field. Inferred from ASS(X, Y, Z), any semantic expression target in ASS(X, Y, Z) cannot contain more than three free variables numbers.

Semantics of Plane

Three-dimensional space 3D can be intuitively regarded as the set whole {PL} of plane PL along any real coordinate r. In such a three-dimensional space, the existence meaning of exPL is the existence correspondence of a (PL, r) pair of R values.

Because of the equivalence of coordinate transformation, this R is equivalent to any one of variable X, variable Y and variable Z.. So we can get that the semantics of the plane is the semantic space PL(X, Y) of two variables after one variable is determined in the three-dimensional space.

Semantics of Lines

Two-dimensional space 2D can be intuitively regarded as the set whole {L} of straight line L along any real coordinate r. In such a two-dimensional space, the existential significance of exL is the existential correspondence of R value R of a (L, R) pair.

Because of the equivalence of coordinate transformation, this R is equivalent to any one of variable X and variable Y.. So we can get that the semantics of a straight line is the semantic space L(X) of a variable after a variable is determined in a two-dimensional space.

Semantics of Point

When any point p in a line COD(X) is cognitively determined, the corresponding point can also be semantically described as ASS(P, COD(X)). From abstract reasoning, it can be directly obtained that any certain point, regardless of the surface semantics of the concept, has only one certain existential semantics exP.

One-dimensional space 1D can be intuitively regarded as the set whole {P} of point p along any real coordinate r. In such a one-dimensional space, the existential significance of exP is the existential correspondence of the R value R of a (p, r) pair.

Therefore, we can get that the semantics of a point is the semantic space P corresponding to the unique variable value X in a one-dimensional space.

Through the frameworks of Existence Computation and Reasoning (EXCR) and Essence Computation and Reasoning (ESCR), we re-examine the semantic relations of points, lines and surfaces in mathematics. This framework provides a new perspective, which enables us to understand the relative semantic relationship between these geometric objects more deeply.

Starting from the semantic space, we re-examine the expressions of points, lines and surfaces in different dimensions and their semantic relations. This re-examination is helpful for us to better understand the construction process of mathematical knowledge, and the roles of Existence Computation and Reasoning (EXCR) and Essence Computation and Reasoning (ESCR) in explaining geometry.

Existence Computation and Reasoning (EXCR) focuses on determining the expression of semantic space from the perspective of cognitive intuition. This method is helpful for us to deeply understand the construction process of mathematical knowledge, and provides a cognitive explanation. At the same time, Essence Computation and Reasoning (ESCR) emphasizes the traceability and expression balance of instantiation at the semantic level of types, which enables us to identify and explain practical problems more accurately.

Through this framework, we realize the balance between subjectivity and objectivity of mathematical knowledge. Although mathematical axioms are based on certain assumptions, they construct an objective mathematical system and provide a powerful tool for explaining the real world. At the same time, the methods of Existence Computation and Reasoning and Essence Computation and Reasoning enable us to understand the essence of mathematical knowledge more deeply, as well as their applications and limitations in explaining geometry and other fields.

In a word, the subjectivity of mathematics is not to belittle its knowledge, but to have a deeper understanding of its essence. Through the application of DIKWP model and semantic mathematics, we can fully understand the construction process of mathematical axioms and their functions and limitations in practical application. By revealing the subjectivity of mathematical knowledge, we can better understand how to effectively apply mathematical tools to solve practical problems, and keep a clear understanding of the nature of knowledge in the process. This understanding helps us to make more informed decisions in an increasingly complex world and gain deeper insights in the fields of science, technology and philosophy. Existence Computation and Reasoning and Essence Computation and Reasoning provide us with more in-depth mathematical thinking tools to better understand and explore the mysteries of mathematics.

5 Application of DIKWP application cases of Essence Computation and Reasoning, Existence Computation and Reasoning, and purpose Computation and Reasoning

5.1 DIKWP application cases of Essence Computation and Reasoning, Existence Computation and Reasoning, and purpose Computation and Reasoning

When the resource transformation in DIKWP model is combined with three key technologies (Essence Computation and Reasoning, Existence Computation and Reasoning, and purpose Computation and Reasoning), more efficient cognition and decision-making can be realized in various fields. The following is an expansion and refinement of the above cases one by one, and each case will discuss its application, advantages and possible challenges in detail.

5.1.1 Case 1: medical diagnosis and treatment decision

In the field of medical diagnosis and treatment decision-making, the three key technologies of resource transformation and integration of DIKWP model have played an important role in helping doctors improve the quality of patient care. The following is the detailed development of this case:

Application scenario

Doctors are faced with complex tasks of diagnosing diseases, making treatment plans and tracking patients' progress. Traditionally, doctors rely on their own experience and medical knowledge to make decisions. However, in modern medical care, a large number of cases and research data become available, and doctors need to effectively process and use this information.

Application of technology fusion

Essence Computation and Reasoning: doctors can use Essence Computation and Reasoning to deeply understand the patient's condition. For example, when facing a cancer patient, doctors can use this technology to understand the essence properties of cancer cells, including their growth rate, diffusibility and so on. This helps doctors to better understand the nature of the disease.

Existence Computation and Reasoning: doctors can associate the patient's case with the existing medical knowledge to confirm the existence of the patient's condition. By comparing patients' clinical data with patients' cases in the database, doctors can more accurately determine the types and severity of diseases suffered by patients.

Purpose Computation and Reasoning: doctors can use purpose Computation and Reasoning to define the goal and plan of treatment. This includes defining the patient's treatment plan, surgical plan and rehabilitation goals. Through this technology, doctors can ensure that the treatment is targeted to maximize the patients' chances of recovery.

Advantages

Accurate diagnosis: By deeply understanding the essence attributes of diseases, doctors can make more accurate diagnosis and avoid subjectivity and misjudgment.

Personalized treatment: The integrated technology enables doctors to make personalized treatment plans according to the unique situation of each patient and improve the treatment effect.

Knowledge sharing: doctors can share their diagnosis and treatment experience with other medical professionals, which promotes the dissemination and cooperation of medical knowledge.

Potential challenges

Data privacy and security: Medical data contains sensitive information, so extra measures must be taken to ensure data security and privacy.

Technical training: Doctors need to be trained to understand and use these new technical tools.

Ethical and legal issues: When using these technologies, ethical and legal issues need to be considered, such as the distribution of responsibilities and the transparency of medical decision-making.

5.1.2 Case 2: financial risk management

The field of financial risk management is another field that can benefit from the resource transformation and technology integration of DIKWP model. The following is a detailed development:

Application scenario

One of the main tasks in the financial field is risk management, including market risk, credit risk and operational risk. Investors and financial professionals need to accurately assess and manage these risks in order to make wise investment decisions.

Application of technology fusion

Essence Computation and Reasoning: financial professionals can use Essence Computation and Reasoning to deeply understand the essence attributes of assets, such as stocks, bonds or commodities. This helps them to understand the root causes of market trends and better predict future price fluctuations.

Existence Computation and Reasoning: financial practitioners can use existence Computation and Reasoning to confirm the existence of specific risks, such as market fluctuation or credit default. By comparing the current market data with historical data, they can better understand the current risk situation.

purpose Computation and Reasoning: investors can use purpose Computation and Reasoning to formulate investment strategies and goals. This includes determining portfolio composition, risk preference and long-term goals. Through this technology, investors can diversify their investments more wisely and reduce risks.

Advantages

Accurate risk assessment: By deeply understanding the nature of assets and market risks, financial practitioners can assess risks more accurately.

Intelligent investment decision: investors can make intelligent investment decisions according to personalized investment goals and strategies to improve the return on investment.

Market monitoring: Integrated technology allows real-time monitoring of market conditions, enabling financial institutions to respond more quickly.

Potential challenges.

Data quality: Accurate risk assessment depends on high-quality data, so it is necessary to ensure the accuracy and integrity of data.

Complexity: Financial markets and investment strategies can be very complex and require highly specialized knowledge and tools.

Market uncertainty: the financial market itself is full of uncertainty, and technical tools cannot eliminate this uncertainty.

5.1.3 Case 3: scientific research and innovation

The field of scientific research and innovation can also benefit from the resource transformation and technology integration of DIKWP model, which is detailed as follows:

Application scenario

Scientists are faced with the challenge of understanding nature, exploring the unknown and innovating. Scientific research needs a deep understanding of the nature of phenomena in order to establish new knowledge and explanations.

Application of technology fusion

Essence Computation and Reasoning: Scientists can use Essence Computation and Reasoning to deeply understand the essence properties of physical, chemical or biological phenomena. This helps them to understand basic principles, such as biochemical processes or astrophysical phenomena.

Existence Computation and Reasoning: Scientists can use Existence Computation and Reasoning to confirm the existence of experimental results and research results. By comparing with previous experimental data, they can verify new scientific findings.

Purpose Computation and Reasoning: Scientists can use purpose Computation and Reasoning to make the next research goals and plans. This includes designing experiments, analyzing data and promoting scientific progress.

Advantages

Deep understanding of nature: By deeply understanding the nature of phenomena, scientists can gain a deeper understanding of nature.

Effective research: Integrated technology can help scientists make research plans and experimental designs more effectively.

Innovation promotion: Scientists can use purpose Computation and Reasoning to promote innovation, develop new technologies and solve complex problems.

Potential challenges.

Complexity: Some scientific fields are very complex and require highly specialized knowledge and technology, as well as a lot of data processing.

Experimental verification: Existence Computation and Reasoning may require a lot of experiments and verification to confirm new scientific discoveries.

Ethical issues: In some research fields, ethical issues may need extra attention, such as biotechnology or human gene editing.

5.1.4 Case 4: project management and decision making

The field of project management can also benefit from the resource transformation and technology integration of DIKWP model, which is developed in detail as follows:

Application scenario

The project manager needs to plan, implement and monitor the project to ensure that the project is delivered on time and completed within the budget. They need to make complex decisions to meet various challenges.

Application of technology fusion

Essence Computation and Reasoning: Project managers can use Essence Computation and Reasoning to deeply understand the essence attributes and requirements of the project. For example, in construction projects, they can learn the characteristics of different building materials to determine the best choice.

Existence Computation and Reasoning: Project managers can use existence Computation and Reasoning to confirm the existence and current status of the project. By comparing with the project plan and timetable, they can know whether the project is going as planned.

Purpose Computation and Reasoning: Project managers can use purpose Computation and Reasoning to formulate project execution plans and goals. This includes determining resource allocation, risk management strategy and problem solving plan.

Advantages

Project success: By deeply understanding the essence attributes of the project, the project manager can better plan and execute the project and ensure the success of the project.

Resource optimization: Integrated technology can help project managers allocate resources better, reduce costs and improve efficiency.

Risk management: purpose Computation and Reasoning can help project managers manage risks better and make plans to solve problems.

Potential challenges.

Change management: changes in the project may have adverse effects on the project, and appropriate change management is needed.

Teamwork: It is very important for the success of the project, but the integrated technology also needs the cooperation and training of the project team.

5.1.5 Case 5: intelligent robot and automation system

Intelligent robots and automation systems are another field, which can make full use of the resource transformation and technology integration of DIKWP model. The following is a detailed development:

Application scenario

Intelligent robots and automation systems are widely used in manufacturing, logistics, medical care and services. They need to be able to recognize and understand the environment and make intelligent decisions to perform tasks.

Application of technology fusion

Essence Computation and Reasoning: Intelligent robots can use Essence Computation and Reasoning to deeply understand the essence properties of objects, such as size, shape and material. This helps them to perform tasks better, such as grabbing and manipulating objects.

Existence Computation and Reasoning: The automatic system can use existence Computation and Reasoning to confirm the existence in the environment, such as the detected obstacles or target objects. By comparing with maps or databases, they can determine the position and identity of objects.

Purpose Computation and Reasoning: Intelligent robots can use purpose Computation and Reasoning to make action plans to perform tasks, such as autonomous navigation or industrial tasks. This includes path planning, obstacle avoidance and task priority.

Advantages

Efficient automation: The integrated technology enables the automation system to perform tasks more intelligently and improve production efficiency.

Substitution of dangerous tasks: Intelligent robots can be used to perform dangerous tasks, such as fire fighting or dangerous goods handling, to reduce personnel risks.

Customized tasks: purpose Computation and Reasoning can help automation systems adapt to different tasks and environments and provide highly customized solutions.

Potential challenges.

Perception and environmental understanding: Intelligent robots need highly accurate perception and environmental understanding ability to avoid collision and perform tasks.

Data processing and Computation capabilities: Intelligent robots and automation systems need powerful data processing and Computation capabilities to support complex decision-making and task execution.

Ethical issues: In the automation system, ethical issues also need additional consideration, such as ethical principles and responsibilities in autonomous decision-making.

These cases show in detail the application, advantages and challenges of resource transformation and the integration of three key technologies in DIKWP model in different fields. By integrating Essence Computation and Reasoning, Existence Computation and Reasoning, and purpose Computation and Reasoning, we can realize smarter and more efficient cognition and decision-making, and promote innovation and progress. In different fields, this integration provides more opportunities for individuals and organizations to deal with complex problems and challenges.

5.2 Semantic mathematics and DIKWP: the age of empowerment information

In the information age, we are faced with a huge amount of information and diversity. It is a challenging task to process this information and turn it into useful knowledge and wisdom. In order to meet this challenge, many technologies and methods have emerged, including semantic mathematics, Essence Computation and Reasoning, Existence Computation and Reasoning, and purpose Computation and Reasoning. This chapter will deeply study these technologies and how they are combined with the DIKWP model to provide powerful tools and frameworks for information processing and application.

5.2.1 Semantic Mathematics: the mathematical basis for understanding and expressing semantics

Semantic mathematics is a mathematical discipline that studies the meaning of symbols and languages, aiming at transforming languages, symbols and concepts into mathematical objects for accurate processing and reasoning. In the field of information processing, semantic mathematics plays a vital role, especially in dealing with unstructured data, natural language texts and knowledge representation.

Key aspects of Semantic Mathematics

Establishment of semantic model

Semantic mathematics technology allows us to establish a clear semantic model and express the semantic meaning of resources as a mathematical structure. These model are usually based on theories such as formal logic, ontology and semantic network, which are helpful to capture the relationship and meaning between resources. The semantic model provides a foundation for the semantic transformation of information, which makes it easier for information to be understood and applied.

Giving semantic labels

Under the framework of semantic mathematics, each resource can be given specific semantic tags, which define the meaning, attributes and relationships of resources. This helps to liberate resources from individual subjective understanding and make them objective. Through semantic tags, we can transform information from original data and text into knowledge fragments with clear semantics.

Semantic mapping

Semantic mathematics technology allows us to formalize the semantic mapping between resources to capture the association and interaction between resources. This is helpful to the semantic fusion between resources, so that different resources can complement and enrich each other and form a more comprehensive cognition.

Ambiguity elimination

Semantic mathematics technology can help identify and eliminate ambiguity in resources and ensure that the meaning of resources can be accurately explained in different contexts. Through disambiguation, we can improve the accuracy and consistency of information interpretation.

Application case

Semantic transformation of data

For example, consider a temperature data set generated by a weather sensor. In the traditional concept space, these data may be just a set of numbers, such as "28.5°C" or "72°F". However, through semantic mathematics technology, these data can be given specific semantic labels, such as "temperature", and their meanings can be defined, that is, indicating the heat or coldness of the environment. In this way, people can understand the actual meaning of these data without knowing the unit or symbol of temperature deeply. This semantic transformation makes it easier for data to be applied in different contexts, such as meteorological prediction, climate research and architectural design.

Deep interpretation of information

For example, consider a medical report that describes the symptoms and diagnosis of patients. In the traditional concept space, these descriptions may only be some text fragments, such as "headache", "fever" and "diagnosis of influenza". However, through semantic mathematics technology, these descriptions can be transformed into semantic entities of medical terms, such as "headache" can be associated with "head pain", "fever" can be associated with "elevated body temperature" and "flu" can be associated with "cold virus infection". This semantic transformation makes medical reports easier to interpret and analyze, and doctors and researchers can directly relate them to medical knowledge without guessing the meaning of symptoms.

Complete semantics of knowledge

For example, consider a knowledge base that contains information about historical events, scientific theories and cultural knowledge. In the traditional concept space, these knowledge fragments may be scattered and lack of clear relationship. However, through semantic mathematics technology, these knowledge fragments can be integrated and connected to form a complete semantic network. Knowledge base can relate different historical events to time axis, scientific theories to related experiments and discoveries, and cultural knowledge to historical background and influence. This semantic transformation makes knowledge easier to understand, not only to find specific information, but also to understand the logic and relationship behind knowledge.

5.2.2 Essence Computation and Reasoning: reveal the essence and internal relationship of things.

Essence Computation and Reasoning is a kind of Computation and Reasoning method that pays attention to the essence and internal relationship of things. It aims to reveal the essence attributes and laws of things, so as to help us understand and analyze information more deeply. Essence Computation and Reasoning are interrelated with semantic mathematics, which reveals the essence characteristics of things through semantic transformation.

Key aspects of Essence Computation and Reasoning

Extraction of essence attributes

Analysis of internal relations

Essence Computation and Reasoning emphasize the internal relationship between things. Through semantic transformation, we can capture the relationship between resources and knowledge and reveal the interaction and influence between things. This helps to understand the essence of things more comprehensively.

Construction of essence model

Application case

Scientific research

In the field of scientific research, Essence Computation and Reasoning can be used to reveal the essence attributes of natural phenomena and scientific theories. Through semantic transformation, scientists can transform experimental data and observation results into descriptions of essence attributes, so as to understand the laws of nature more deeply.

Product design

In product design, Essence Computation and Reasoning can help designers reveal the essence requirements and characteristics of products. Through semantic transformation, product requirements and user feedback can be transformed into descriptions of product essence attributes to guide product design and improvement.

Intelligence analysis

The field of intelligence analysis can reveal the essence characteristics of events and intelligence by means of Essence Computation and Reasoning. Through semantic transformation, intelligence analysts can integrate information from different sources and reveal the essence relationship of events, which is helpful to understand the dynamics and reasons behind events more deeply.

5.2.3 Existence Computation and Reasoning: Understanding the Existence State of Things

Existence Computation and Reasoning is a Computation and Reasoning method that pays attention to the existing state of things. It aims to understand the existence, availability and accessibility of things to support the accuracy and reliability of information. Existence Computation and Reasoning are intertwined with semantic mathematics, which reveals the existing state of things through semantic transformation.

There are key aspects of Computation and Reasoning.

Representation of existence state

Accessibility analysis

The Construction of Existence Model

Application case

Data quality management

In the field of data management, Existence Computation and Reasoning can be used to evaluate the reliability and accuracy of data. Through semantic transformation, the source and quality information of data can be expressed as existence state, which helps data managers understand the credibility of data.

Internet of things equipment monitoring

In the field of Internet of Things, Existence Computation and Reasoning can be used to monitor the status and availability of devices. Through semantic transformation, the sensor data of IOT devices can be transformed into the description of the existing state, which is helpful for real-time monitoring and fault diagnosis.

Knowledge map maintenance

Knowledge map maintenance needs to understand the existing state and related information of knowledge entities. Through semantic transformation, the entities in the knowledge map can be represented as the existing state, which helps to update and maintain the knowledge map.

5.2.4 Purpose Computation and Reasoning: understanding and inferring behaviors and purposes.

Purpose Computation and Reasoning is a Computation and Reasoning method that focuses on behavior and purpose. It aims to understand the purpose behind human behavior and decision-making to help

Help to understand and explain human activities more deeply. purpose Computation and Reasoning are intertwined with semantic mathematics, which reveals the logic behind behavior and purpose through semantic transformation.

Key aspects of purpose Computation and Reasoning

Purpose modeling: purpose Computation and Reasoning involve establishing a clear purpose model to describe the purpose and motivation of human behavior. Through semantic transformation, behaviors and decisions can be mapped to specific purposes, such as shopping, learning, entertainment and so on.

Behavior analysis: This field emphasizes the analysis of behavior and decision-making to understand the relationship between them and purpose. Through semantic transformation, we can interpret behavior as the expression of purpose and help to understand the purpose behind behavior.

purpose inference: purpose Computation and inference also include the inference of purpose, that is, the inference of human purpose based on observed behavior and contextual information. Through semantic transformation, we can map the observed behavior to possible purposes, so as to improve the accuracy of purpose inference.

Application case

User behavior analysis: In online advertising and e-commerce, purpose Computation and Reasoning can be used to analyze users' browsing and purchasing behaviors to understand their shopping purposes, so as to provide personalized recommendations and advertisements.

Intelligent assistant: purpose Computation and Reasoning can be used to build intelligent assistants, such as voice assistants and chat robots, to understand users' purposes and needs and provide corresponding help and response.

Security monitoring: In the field of network security, purpose Computation and Reasoning can be used to monitor user and system behaviors to detect potential threats and attack purposes.

Autonomous driving: In autonomous vehicles, purpose Computation and Reasoning can be used to analyze the behaviors of surrounding vehicles and pedestrians, so as to predict their purposes and make corresponding driving decisions.

5.2.5 Combination of Semantic Mathematics and DIKWP

Semantic mathematics provides a solid foundation for the DIKWP model. Formally expressing the semantics of knowledge, information, data and behavior is helpful to establish a richer and deeper knowledge system. Combined with DIKWP, semantic mathematics can empower all aspects of the information age and achieve the following key goals:

Complete semantic transformation of knowledge: semantic mathematics provides DIKWP with the method and technology of complete semantic transformation of knowledge. It can transform data, information and knowledge into a form with clear semantics, helping people to understand and apply knowledge more easily.

Essence Computation and Reasoning: Semantic mathematics can be used to reveal the essence attributes and laws of knowledge and information and help people understand the essence of things more deeply. Combined with DIKWP, it can support more intelligent knowledge management and reasoning.

Existence Computation and Reasoning: Semantic mathematics can be used to express the existence state and reliability of knowledge and information. In DIKWP, it can support the accessibility analysis of resources and knowledge, and improve the accuracy and credibility of information.

Purpose Computation and Reasoning: the combination of semantic mathematics and DIKWP can help to understand and infer human behavior and purpose. This is widely used in intelligent assistant, safety monitoring and automatic driving.

Semantic mathematics, Essence Computation and Reasoning, Existence Computation and Reasoning, and purpose Computation and Reasoning are the key technologies in the information age. They provide powerful tools and frameworks for knowledge processing, intelligent decision-making and human-computer interaction through semantic transformation, essence analysis, Existence state representation and purpose inference. The combination with DIKWP model makes these technologies more powerful and provides comprehensive support for the enrichment, deepening and intelligence of knowledge. This comprehensive technical framework is expected to have a far-reaching impact in various fields and promote the development and progress of the information age. In the future, we can expect more innovations and applications to solve complex problems and challenges and realize smarter and more efficient knowledge management and application methods.

5.3 The value and role of semantic mathematics in the development of digital economy in Hainan Free Trade Port

With the continuous development and application of information technology, digital economy has become an important part of modern economy. As a national pilot free trade zone, China Hainan Free Trade Port is actively promoting the development of digital economy. Digital economy involves a lot of data, information, knowledge and behavior, so it needs powerful technology and framework to process and analyze these resources to support economic growth and innovation. This chapter will discuss the value and function of semantic mathematics, Essence Computation and Reasoning, Existence Computation and Reasoning, and purpose Computation and Reasoning in the development of digital economy in Hainan Free Trade Port, and how they can empower all aspects of digital economy.

5.3.1 Semantic Mathematics: the mathematical basis for understanding and expressing semantics

Key aspects of semantic mathematics

Establishment of semantic model

In the digital economy, massive data and information need to be understood and transformed into useful knowledge. Semantic mathematics allows us to establish a clear semantic model and express the semantic meaning of data and information as a mathematical structure. This helps to capture the relationship and meaning between resources, provides a foundation for semantic transformation of data and information, and makes it easier to be understood and applied.

Giving semantic labels

Under the framework of semantic mathematics, each data and information resource can be given a specific semantic tag, which defines the meaning, attributes and relationships of the resource. Through semantic tags, we can transform data and information from the original state into knowledge fragments with clear semantics, thus making them objective and easier to share and apply.

Semantic mapping

Semantic mathematics technology allows us to formalize the semantic mapping between different data and information resources to capture the correlation and interaction between resources. This is helpful to the semantic integration between resources, so that different resources can complement and enrich each other and form a more comprehensive cognition, which provides support for the knowledge integration of digital economy.

Ambiguity elimination

Application case

Semantic transformation of data

In the digital economy of Hainan Free Trade Port, a large amount of data needs to be semantically transformed for better understanding and application. For example, Hainan Free Trade Port may involve a large amount of trade data. Through semantic mathematics technology, these data can be given specific semantic labels, and their meanings and relationships can be defined, making it easier to understand and analyze. This will help to support the formulation and optimization of trade policies and improve the competitiveness of Hainan Free Trade Port.

5.3.2 Essence Computation and Reasoning: reveal the essence and internal relationship of things.

Key aspects of Essence Computation and Reasoning

Extraction of essence attributes

Essence Computation and Reasoning are devoted to extracting the essence attributes of things from data, information and knowledge. Through semantic transformation, we can extract the key features and attributes from information to form a richer and deeper description of things. In the digital economy, it is helpful to reveal the essence laws and characteristics of economic activities and provide more insightful information for decision makers.

Analysis of internal relations

Essence Computation and Reasoning emphasize the internal relationship between things. Through semantic transformation, we can capture the relationship between resources and knowledge and reveal the interaction and influence between things. This will help to understand the economic system and market dynamics in the digital economy more comprehensively and provide better strategic decision support for enterprises and governments.

Construction of essence model

Essence Computation and Reasoning usually involve the construction of essence model to describe the essence attributes and laws of things. These model can be established on the basis of semantic mathematics, combining the semantic information of things with their essence characteristics. In the digital economy, the essence model can be used to predict market trends, consumer behavior and industrial development, and provide strong support for economic planning and strategy formulation.

Application Case

Economic projection

In the digital economy of Hainan Free Trade Port, Essence Computation and Reasoning can be used to reveal the essence attributes and laws of economic activities. Through semantic transformation, economic data can be transformed into descriptions of essence attributes, which can help analyze market trends and industrial development and provide more accurate economic forecasts for the government and enterprises.

Financial risk management

Essence Computation and Reasoning can also play a key role in the financial field. By analyzing the internal relations and essence attributes of financial markets, we can better identify and manage financial risks and provide more reliable risk assessment and decision support for investors and financial institutions.

5.3.3 Existence Computation and Reasoning: Understanding the Existence State of Things

There are key aspects of Computation and Reasoning.

Representation of existence state

Existence Computation and Reasoning involve expressing the existing state of things. Through semantic transformation, we can express the existence, availability and accessibility of things with clear semantic tags, thus helping to identify and understand the existing state of things. In the digital economy, this is very important to ensure the reliability and accuracy of data, especially when transactions and contracts are involved.

Accessibility analysis

Existence Computation and Reasoning emphasize the accessibility and correlation between things. Through semantic transformation, we can analyze the relationship between resources and knowledge, reveal the connections and paths between things, and help to understand the accessibility of things. In the digital economy, accessibility analysis can be used to optimize supply chain management, logistics planning and customer relationship management to improve operational efficiency.

The Construction of Existence Model

Existence Computation and Reasoning usually need to build an Existence model to describe the existing state and related information of things. These model can be established on the basis of semantic mathematics, combining the semantic information of things with their existing States. In the digital economy, the existence model can be used to establish a digital identity and data traceability system to ensure the source and credibility of data.

Application Case

Data quality management

In the digital economy, data quality management is very important. Existence Computation and Reasoning can be used to evaluate the reliability and accuracy of data. Through semantic transformation, the source and quality information of data can be expressed as existence state, which helps data managers to understand the credibility of data and thus improve the reliability of decision-making.

Internet of things equipment monitoring

In the field of Internet of Things, Existence Computation and Reasoning can be used to monitor the status and availability of Internet of Things devices. Through semantic transformation, the sensor data of IOT devices can be transformed into the description of the existing state, which is helpful for real-time monitoring and fault diagnosis, and improves the reliability and performance of devices.

5.3.4 Purpose Computation and Reasoning: Understanding and inferring behaviors and purposes.

Key aspects of Purpose Computation and Reasoning

Purpose modeling

purpose Computation and Reasoning involve establishing a clear purpose model to describe the purpose and motivation of human behavior. Through semantic transformation, behaviors and decisions can be mapped to specific purposes, such as shopping, learning, entertainment and so on. In the digital economy, understanding the purposes of consumers and market participants is very important for accurate marketing and product promotion.

Behavior analysis

This field emphasizes the analysis of behavior and decision-making to understand the relationship between them and purpose. Through semantic transformation, we can interpret behavior as the expression of purpose and help to understand the purpose behind behavior. In the digital economy, analyzing user behavior can provide support for personalized recommendation and advertising.

Purpose inference

Purpose Computation and Reasoning also includes the inference of purpose, that is, inferring human purpose according to observed behavior and contextual information. Through semantic transformation, we can map the observed behavior to possible purposes, so as to improve the accuracy of purpose inference. In the digital economy, understanding the purposes of market participants can provide strong support for market analysis and forecasting.

Application Case

User behavior analysis

In the digital economy, understanding users' behaviors and purposes is very important for providing personalized services and products. purpose Computation and Reasoning can be used to analyze users' browsing and purchasing behaviors to understand their shopping purposes, so as to provide personalized recommendations and advertisements and promote sales growth.

Intelligent assistant

purpose Computation and Reasoning can be used to build intelligent assistants, such as voice assistants and chat robots, to understand users' purposes and needs and provide corresponding help and response. In the digital economy, intelligent assistants can be used to provide customer support, online consultation and automation services.

5.3.5 Comprehensive application of semantic mathematics, Essence Computation and Reasoning, Existence Computation and Reasoning, and purpose Computation and Reasoning.

Semantic mathematics, Essence Computation and Reasoning, Existence Computation and Reasoning, and purpose Computation and Reasoning are the key technologies and methods in the development of digital economy, which are intertwined and provide comprehensive support for economic activities and markets. The comprehensive application of these technologies can achieve the following goals:

Complete semantic transformation of knowledge: these technologies can transform data, information and knowledge into a form with clear semantics, help people understand and apply knowledge more easily, and support knowledge management and innovation in the digital economy.

Essence Computation and Reasoning: These technologies can reveal the essence attributes and laws of economic activities in the digital economy, help the government and enterprises better understand the market and industry, and provide more insightful information for strategic planning and decision-making.

Existence Computation and Reasoning: These technologies can help to ensure the reliability and accuracy of data in the digital economy, and help to improve the credibility of data by expressing the existence state of data, thus supporting transactions and contracts in the digital economy.

purpose Computation and Reasoning: These technologies can help to understand the behaviors and purposes of market participants in the digital economy, thus supporting personalized marketing and product promotion and promoting sales growth.

Comprehensive application of these technologies can provide powerful technical support and intelligent decision-making for the development of digital economy in Hainan Free Trade Port. With the help of semantic mathematics, data and information can be transformed into knowledge with clear semantics, making it easier to understand and apply. Essence Computation and Reasoning reveal the essence law of economic activities in digital economy, which provides better support for strategic planning. The existence of Computation and Reasoning helps to ensure the reliability of data and supports transactions and contracts in the digital economy. purpose Computation and Reasoning help to understand the behaviors and purposes of market participants and support personalized marketing and product promotion.

In the development of digital economy in Hainan Free Trade Port, semantic mathematics, Essence Computation and Reasoning, Existence Computation and Reasoning, and purpose Computation and Reasoning have provided strong support for economic activities and markets. They help to realize the semantic transformation of data and information, reveal the essence attributes and laws of economic activities, ensure the reliability of data, and understand the behaviors and purposes of market participants. The comprehensive application of these technologies will empower all aspects of the digital economy in Hainan Free Trade Port, promote economic growth and innovation, and lay a solid foundation for the future development of digital economy.

5.4 DIKWP and Semantic Mathematical Analysis "The Confluent Analects" "Gentleman is harmonious but different, while petty people are the same but not harmonious."

As a treasure of ancient China culture, The Confucian Analects is rich in morality and code of conduct. Among them, the statement "Gentleman is harmonious but different, while petty people are the same but not harmonious" reflects Confucius' unique views on interpersonal relationship and social harmony. However, how to apply this classical wisdom to modern society is still a problem worth discussing. In this paper, DIKWP model and semantic mathematics analysis method are used to deeply understand the connotation of this sentence and apply it to modern life situations. Through this interdisciplinary research method, we expect to provide a new perspective and guidance for the development of individuals and society.

5.4.1 Application analysis of DIKWP model and semantic mathematics in "Gentleman is harmonious but different, while petty people are the same but not harmonious."

As one of China's ancient cultural classics, The Confident Analects contains many wisdom proverbs about morality, code of conduct and interpersonal relationships. Among them, "Gentleman is harmonious but different, while petty people are the same but not harmonious." is a classic exposition, which reflects Confucius' understanding of interpersonal relationship and social harmony. In this paper, the meaning of this sentence will be discussed in depth by combining DIKWP model and semantic mathematics analysis method, and it will be applied to modern society to provide useful guidance for personal and social development.

Data analysis

Data objectification:

Under the framework of semantic mathematics, we can regard the behavior patterns of "gentlemen" and "petty people" as quantifiable and analyzable data. These data can come from historical materials, philosophical works, social observation and other sources. Through objective data analysis, we can more accurately understand the connotation of "gentle are harmonious but different, while petty people are the same but not harmonious."

For example, by analyzing the historical documents and philosophical works of Confucius' era, we can obtain the behavior records of "gentlemen" and "petty people", such as the words and deeds of Confucius and his students, the interaction with others, social status and other data. These data will provide us with an objective description of these two behavior patterns, and avoid possible misunderstandings caused by subjective prejudice and cultural differences.

Data unified interpretation:

In data analysis, the method of semantic mathematics can help us to interpret the behavioral data of "gentlemen" and "petty people" in a unified way. This step is crucial, because different cultures, backgrounds and viewpoints may lead to different interpretations of these data.

Through the framework of semantic mathematics, we can establish a consensus explanation model to ensure that different people can draw consistent conclusions when facing the same data. This helps to eliminate the subjectivity of interpretation, and enables us to understand the meaning of "gentle are harmonious but different, while petty people are the same but not harmonious." more objectively.

Information processing

Information semantic analysis:

The information level in DIKWP model involves in-depth semantic analysis of data. At this stage, we will pay attention to the information content of "Gentleman is harmonious but different, while petty people are the same but not harmonious." and its significance in the social and cultural background.

For example, we can analyze the social background and cultural values of Confucius era and understand the social and cultural significance of "harmonious but different" and "same but not harmonious". This will help us to better grasp the connotation of this sentence, not just the superficial data description.

Objective transmission of information:

Semantic mathematics not only helps us to understand information deeply, but also helps us to transmit information objectively. In the process of information transmission, it is often influenced by subjective interpretation and misunderstanding, which leads to distortion and inaccuracy of information.

Through the analysis framework of semantic mathematics, we can ensure that information is transmitted to others in a more objective and accurate way, and reduce misunderstandings and ambiguities in the process of information transmission. This helps to ensure that the wisdom of "Gentleman is harmonious but different, while petty people are the same but not harmonious." can be conveyed to modern society in the most authentic form.

Knowledge construction

Knowledge network construction:

In DIKWP model, the knowledge level involves the organization of information and the construction of knowledge system. By analyzing the information of "Gentleman is harmonious but different, while petty people are the same but not harmonious.", we can build a knowledge network about personal morality and social behavior.

This knowledge network can include knowledge about the characteristics, moral principles, social influence and other aspects of "gentlemen" and "petty people". In this way, we can more systematically understand the connotation of this sentence and its significance in different historical periods and cultural backgrounds.

knowledge deep understanding:

The method of semantic mathematics helps us to deeply understand the teachings of Confucius and its deep-seated significance in different historical periods and cultural backgrounds. Through the deep understanding of knowledge, we can better grasp the wisdom of "gentle are harmonious but different, while petty people are the same but not harmonious."

For example, we can explore the guiding role of this sentence in personal moral cultivation and social harmony, and its practical application in modern society. This will provide us with more insightful theoretical support and practical guidance.

Wisdom application

Ethics and morality objective analysis:

In DIKWP model, the intelligence level involves the objective analysis of ethics and morality. Through semantic mathematics, we can understand the ethical and moral meanings of the behavior patterns of "gentlemen" and "petty people", and their influences on personal and social development.

For example, we can analyze the guiding role of this sentence in personal moral cultivation and social harmony, as well as its shaping of ethical concepts. This will help us to better understand the ethical wisdom of "Gentleman is harmonious but different, while petty people are the same but not harmonious."

Guidance for decision-making and action:

Applying the wisdom of "while petty people are the same but not harmonious." to modern decision-making and action can provide moral and ethical guidance for modern society. For example, in government decision-making, business management, social management and other fields, the wisdom of this sentence can help people make more wise and ethical decisions.

By incorporating the wisdom of this sentence into the decision-making and actions of modern society, we can promote the harmonious development of individuals and society and enhance social fairness and justice.

Purpose clarification

Clarity of objectives:

In DIKWP model, we analyze the purpose and purpose behind "gentleman is harmonious but different, while petty people are the same but not harmonious." through semantic mathematics. This helps to clarify the guiding goal of this wisdom for personal behavior and social development.

For example, we can analyze that the purpose of this sentence is to encourage people to maintain a harmonious relationship with others while pursuing personal interests, so as to promote social stability and prosperity. This clear goal will help us to better apply the wisdom of this sentence.

Formulation of action plan:

Combined with the analysis results of semantic mathematics, we can make a concrete action plan to realize the application of the concept of "Gentleman is harmonious but different, while petty people are the same but not harmonious." in modern society. This includes popularizing this wisdom in the field of education and guiding people to practice this principle in their behavior.

Through the comprehensive application of DIKWP model and semantic mathematics, we can not only understand the meaning of "while petty people are the same but not harmonious." from the data and information level, but also deeply explore its deep-seated significance in the level of knowledge, wisdom and purpose. This comprehensive analysis provides us with a new perspective, which can not only deeply understand this classical wisdom, but also guide our practical application in modern society and promote the harmonious development of individuals and society. Through the objective analysis of semantic mathematics and the comprehensive application of DIKWP model, we can understand and practice Confucius' teachings more clearly and accurately, which provides a theoretical basis and practical guide for building a more harmonious and moral society.

5.4.2 From the perspective of semantic mathematics, the profound meaning of "Gentleman is harmonious but different, while petty people are the same but not harmonious."

From the perspective of semantic mathematics, the sentence "Gentleman is harmonious but different, while petty people are the same but not harmonious." is not only a description of human behavior, but also a profound insight into the interaction mode of human society. Semantic mathematics regards it as a complex semantic network containing rich sociological, psychological and philosophical information.

Semantic mathematicization of concepts

"gentlemen" and "petty people":

Gentlemen: represents a noble moral character and independent thought, and its behavior mode emphasizes the persistence of individual intrinsic value and the balance of social harmony.

Petty people: refers to those individuals who lack independent thoughts and tend to blindly follow or pursue superficial harmony, but are full of contradictions and conflicts inside.

"harmonious" and "same":

Harmonious: refers to a social relationship based on internal consistency and external differences. This harmony is based on respecting individual differences, emphasizing the balance between independence and symbiosis.

Same: It reflects the apparent consistency, but it may lack deep understanding and real acceptance.

Social genes and cultural evolution

The Cultural Genetic Role of The Confucian Analects:

This sentence in The Confident Analects can be regarded as a part of cultural genes, which conveys important information about social interaction and individual behavior.

This sentence reflects a cultural evolution that spans time and space, and is a key component of human social genes.

The composition of genes in human society:

The genes of human society are not only determined by biological genes, but also shaped by culture, customs and social norms.

This social gene can be inherited and developed through language, culture and education.

Knowledge system Construction

From the perspective of social communication:

From the perspective of social communication, the spread of this sentence shows its importance in social functions, such as social cohesion and cultural inheritance.

The spread of this sentence also reflects the values and expectations of human society for harmony and consistency.

The contribution of philosophy:

From a philosophical point of view, "Gentleman harmonious but different" reflects the great attention to individual freedom and respect for differences, while "Petty people same but not harmonious" reveals the contradictions and conflicts that may be hidden behind the apparent harmony.

This philosophical view has a far-reaching impact on understanding human behavior, social structure and ethical values.

Social interaction and individual behavior

Internal consistency and external difference:

"Gentleman harmonious but different" emphasizes a balance between internal consistency and external differences, which is a social interaction model based on mutual respect and understanding.

This interactive mode is very important for maintaining social stability and promoting individual development and cultural diversity.

Identity and superficial harmony:

The situation of "Petty people same but not harmonious" highlights the superficial identity and the lack of deep understanding of social communication mode. Although this model looks harmonious on the surface, it actually lacks real resonance and mutual understanding.

This reflects a shallow form of social interaction and may lead to internal conflicts and contradictions.

Species development and social evolution

Semantic mathematics and species continuity:

Through the analysis of semantic mathematics, we can understand the influence of "gentle are harmonious but different, while petty people are the same but not harmonious." on the sustainable development of human species. The wisdom and principles contained in this sentence are helpful to guide the evolution of society and the development of individuals.

This wisdom is embodied in how to find a balance between individual differences and social harmony, which is very important for the stability and sustainable development of human society.

Semantic model of human social evolution:

From the perspective of semantic mathematics, the evolution of human society is not only a biological change, but also an evolution of cultural and social communication patterns.

As a part of cultural genes, this sentence reflects the evolutionary trajectory of human beings in social interaction and individual behavior, and is an important symbol of how human beings adapt to the social environment and develop a more advanced social model.

Existence Computation and Essence Computation

Perspective of Existence Computation:

"Gentleman is harmonious but different, while petty people are the same but not harmonious." In the framework of Existence Computation, it is regarded as a computing element of social interaction and individual behavior pattern. This calculation not only covers the description of behavior patterns, but also includes the analysis of the deep-seated reasons and logic behind these patterns.

This calculation helps us to understand the causes and influences of different social communication modes, and how to make more reasonable and effective behavior choices in a complex social environment.

Application of Essence Computation:

The Essence Computation goes further into the core essence of this sentence and discusses how it is embodied in human social structure and psychological model.

This in-depth analysis is helpful for us to understand the essential driving force of human social behavior and how to promote the overall progress of society by improving social communication mode and promoting individual development.

Through the semantic and mathematical analysis of the sentence "Gentleman is harmonious but different, while petty people are the same but not harmonious." in The Confluent Analects, we can not only deeply understand its literal meaning, but also gain insight into its far-reaching significance in sociology, psychology and philosophy. This sentence is not only a description of individual behavior and social communication mode, but also a profound reflection on the evolution of human society and cultural inheritance. Through the analysis of semantic mathematics, we can understand the value and significance of this sentence more comprehensively, and its enlightenment to modern society and future development. This analytical method not only helps to enrich our understanding of ancient wisdom, but also provides guidance and inspiration for modern society, especially in improving the quality of social communication and promoting the harmonious development of individuals and society.

5.4.3 Application of Purpose Computation in "Gentleman is harmonious but different, while petty people are the same but not harmonious."

In the in-depth analysis of the sentence "Gentleman is harmonious but different, while petty people are the same but not harmonious." in The Confident Analects, we introduced the concept of Purpose Computation. This concept focuses on the instance level of semantics, especially in analyzing the consistency and inconsistency between the behavior of gentlemen and petty people and their internal purposes.

Purpose Computation framework

Purpose Computation focuses on understanding and analyzing the deep purpose and purpose behind individual behavior.

In the sentence "Gentleman is harmonious but different, while petty people are the same but not harmonious.", Purpose Computation helps us to explore the internal logic and social influence behind the behavior patterns of Gentleman and Petty People.

Gentleman consistency analysis

Gentleman's behavior pattern (harmonious but different) points out that although Gentleman maintains harmony with others in external behavior, he maintains independence and consistency in internal purposes and values.

Purpose Computation reveals the consistency between individual's external behavior (harmonious) and internal purpose (different) in the category of Gentleman, that is, Gentleman is consistent in behavior and internal values.

Petty People inconsistent analysis

Petty people's behavior pattern (same but not harmonious) shows that although petty people may be the same as others in external behavior, there are conflicts and disharmonies in internal purposes and values.

In the calculation of purpose, this is manifested in the inconsistency between petty people's behavior (same) and not harmonious, that is, petty people sacrificed internal harmony and sincerity in the pursuit of superficial consistency.

Purpose Computation in the social and cultural significance

By analyzing the consistency and inconsistency of the behaviors and inner purposes of gentlemen and petty people, we can have a deeper understanding of sincerity and hypocrisy in social communication and the influence of these behaviors on social culture and individual development.

Purpose Computation reveals the deep motivation and social and cultural significance behind different individual behavior patterns in society, and provides a powerful tool for understanding and evaluating social interaction.

Purpose Computation provides a powerful tool to deeply understand individual behavior and social interaction when analyzing the sentence "Gentleman is harmonious but different, while petty people are the same but not harmonious." It not only helps us understand the superficial phenomenon of individual behaviors, but more importantly reveals the deep purpose and social and cultural meaning behind these behaviors.

Through Purpose Computation, we can more comprehensively understand individual behavior patterns in society and their long-term influence on individual development and social progress, thus providing theoretical basis and practical guidance for promoting healthier and harmonious social interaction.

5.4.4 The in-depth analysis of Purpose Computation in "Gentleman is harmonious but different, while petty people are the same but not harmonious."

When we deeply analyze "Gentleman is harmonious but different, while petty people are the same but not harmonious." in The Confucian Analects, we apply Purpose Computation to explore the deep meaning behind this classical wisdom. Purpose Computation not only pays attention to the behavior itself, but also deeply discusses the internal motivation and social influence behind the behavior.

Purpose Computation basic principle

Conceptual and semantic framework:

Purpose Computation relies on semantic mathematics, which transforms complex social and psychological concepts into mathematical models that can be calculated and analyzed.

Through the in-depth analysis of the concepts of "gentlemen" and "petty people", we can explore the behavior patterns and internal purposes of these roles in social communication.

Mapping from behavior to purpose:

Purpose Computation emphasizes the consistency or inconsistency between behavior and internal purpose, thus revealing the true personality and social adaptability of individuals.

This mapping helps us to understand the deep motivation and cultural value of social behavior.

“gentlemen "and" petty people "behavior patterns in-depth analysis

Harmonious but different from gentlemen:

Gentlemen's behavior pattern shows external harmony and internal independence. This model is an advanced way of social interaction, emphasizing the persistence of individual values and the balance of social harmony.

In the calculation of purpose, the consistency between gentlemen's behavior and internal purpose shows their sincerity and self-esteem. While maintaining their individuality and independence, they strive to maintain harmony with society.

Same but not harmonious by petty people:

Petty people's behavior pattern is characterized by external consistency and internal contradiction. This model is often superficial harmony, with selfishness and conflict hidden behind it.

In the calculation of purpose, the inconsistency between petty people's behavior and internal purpose reveals their hypocrisy and instability. They may pursue superficial consistency, but their hearts are full of contradictions and conflicts.

Application of Purpose Computation in social culture

A deep analysis of social communication:

Purpose Computation not only helps us understand individual behavior patterns, but also reveals the deep significance of these patterns in social culture.

By analyzing the behavior patterns of gentlemen and petty people, we can better understand the relationship between social harmony and individual development, and how to promote the overall progress of society by enhancing individual morality and self-awareness.

Cultural inheritance and social evolution:

The application of Purpose Computation also reflects the cultural inheritance and social evolution. As a part of cultural heritage, this sentence conveys profound insights about the relationship between individuals and society.

This deep analysis helps us to understand the inheritance of cultural values and the evolution of social structure, and how to guide the development of modern society and the improvement of individual behavior according to these opinions.

Case analysis

The following will discuss the practical application and influence of the classical wisdom "Gentleman is harmonious but different, while petty people are the same but not harmonious." in The Confident Analects through classic historical cases. The following cases show the embodiment of this concept in different historical periods and cultural backgrounds.

Case 1: gentlemen and petty people in the Age of Confucius

Confucius' moral teaching:

Confucius lived in the Spring and Autumn Period, which was a period of social unrest and moral decay. Through his teaching, Confucius advocated gentlemen's moral character and behavior standard, and emphasized the importance of inner quality.

Confucius himself is a typical representative of "gentle harmonious but different". He can live in harmony with different people while maintaining personal principles and moral standards.

Petty people's behavior in society at that time:

In the teachings of Confucius, petty people are often those who pursue power and interests at the expense of moral principles and social harmony.

There are many examples in history, such as some officials and nobles, who caused social conflicts and turmoil in the process of pursuing personal interests.

Case 2: Harmony and Differences in the Roman Empire

Roman Republican system:

The political system in the Roman Republic period embodied the idea of "gentle harmonious but different" to a certain extent. Although politicians have different opinions, they can usually work in harmony for the benefit of Rome.

For example, Caesar and Pompey, despite their political differences, can jointly safeguard the stability and prosperity of Rome in a certain period of time.

Unity and conflict in the imperial period:

During the Roman Empire, the superficial unity concealed the deep conflict and discord. The history of this period is often regarded as an example of "Petty people same but not harmonious".

The rule of emperors such as Nero and caligula maintained the unification of the empire on the surface, but actually aggravated social conflicts and dissatisfaction.

Case 3: Freedom of Thought during the Renaissance

Enlightenment of the Renaissance:

Renaissance Europe witnessed the free development of ideas and arts. Thinkers and artists in this period promoted social and cultural harmony while pursuing personal independence and creativity.

Leonardo da Vinci and Michelangelo are typical examples of "gentle harmonious but different". While maintaining their independent creativity, they promoted the cultural progress of the whole era.

Freedom of thought and social harmony:

Renaissance thinkers and artists, through their innovation and independent thinking, not only enriched the cultural life of society, but also promoted ideological pluralism and harmony.

Their work shows how to live in harmony with society and promote cultural and ideological progress while maintaining personal independence.

Through these historical cases, we can see that the concept of "Gentleman is harmonious but different, while petty people are the same but not harmonious." is reflected in different cultures and times. Whether in ancient China, the Roman Empire or Renaissance Europe, this wisdom has its unique expression and practice. These cases prove the universality and epoch-making leap of Confucius, a classical wisdom, and also reveal the importance of pursuing individual independence and social harmony in different historical periods. Through the lens of history, we can deeply understand the significance of this concept and its enlightenment to modern society: while maintaining individuality and principles, we should seek to live in harmony with society and jointly promote social progress and development.

Through the in-depth analysis of "Gentleman is harmonious but different, while petty people are the same but not harmonious.", we can not only fully understand the superficial meaning of this classical wisdom, but also gain insight into the deep social and psychological significance behind it. This analysis helps us to understand individual behavior patterns in society and how these patterns affect individual mental health and the overall development of society. Through Purpose Computation, we can identify and cultivate the gentlemen behavior pattern in society more accurately, and at the same time identify and avoid the bad behavior of petty people, thus promoting the development of society in a more harmonious and healthy direction. Generally speaking, Purpose Computation provides us with a powerful tool, which can not only be used to analyze ancient wisdom, but also be applied to the development of modern society and the guidance of individual behavior, providing a theoretical basis and practical guide for building a more harmonious social relationship and improving individual moral literacy.

Through the in-depth analysis of "Gentleman is harmonious but different, while petty people are the same but not harmonious." in The Confluent Analects, this paper reveals its rich connotation in sociology, psychology and philosophy. By using DIKWP model and semantic mathematics, we not only understand the behavior patterns of gentlemen and petty people, but also discuss the practical application of this sentence in modern society. The research emphasizes the importance of respecting individual differences and pursuing social harmony, which provides valuable guidance for personal moral cultivation and the handling of social relations. By introducing the concept of Purpose Computation, this paper further analyzes the internal motivation and social influence of gentlemen and petty people, and deepens the understanding of this classical wisdom. Through interdisciplinary analysis, this paper provides a new perspective and tool for understanding and practicing the wisdom of "while petty people are the same but not harmonious.", which is of great theoretical and practical significance for promoting the harmonious development of individuals and society.

5.5 DIKWP and Semantic Mathematics in the Case of Ticket Ordering

With the rapid development of information technology, digital service platform plays an increasingly important role in daily life. However, the complexity of user experience and the improvement of service quality remain a challenge. In this paper, the case of one-time ticket booking is taken as the research object, and the problems and difficulties that users may encounter in the process of using the platform are deeply analyzed by introducing the DIKWP model and the theoretical tools of semantic mathematics. Our goal is to understand and improve the user experience of digital service platform and put forward practical optimization strategies. Through the study of this case, we expect to provide valuable reference and enlightenment for the field of digital services and promote the sustainable development and innovation in this field.

5.5.1 The story of ticket ordering

Story summary

At night in Haikou, the breeze is gentle and the stars are shining. Lei Zhang sat on the balcony, thinking about the next day's travel plan. She decided to go to the beautiful Wuzhishan to experience a spiritual baptism. She turned on her mobile phone, clicked on WeChat's travel service and started booking tickets.

A false start

Lei Zhang's fingers slid lightly on the screen, and she carelessly selected the number of trains returning to Haikou from Wuzhishan. The ticket information flashed by, and she quickly clicked "Pay". Soon after, she realized that she had booked the wrong ticket. Anxiety and disappointment are intertwined, and she hurriedly calls customer service, hoping to solve this problem.

Customer service response

From the other end of the phone came the voice of eLong customer service, young and mechanical. The customer service told her that the only solution was to go to Wuzhishan station to refund the ticket in person. Lei Zhang felt helpless. How could she get to Wuzhishan before leaving? She felt trapped in the whirlpool of rules and couldn't extricate herself.

Choice of wisdom

Late at night, Lei Zhang pondered for a long time. She decided to try booking tickets again, this time directly on the official platform of Haiqi. The operation is simple and clear, and there is no extra handling fee. She succeeded in booking the right ticket, and the pressure in her heart was finally released.

An unexpected turn for the better

The next day, Lei Zhang explained his situation with the help of the person in charge and staff of Wuzhishan Station. They showed the enthusiasm and understanding of the people of Hainan Free Trade Port and helped her successfully complete the refund. All this was beyond her expectation, and she felt extremely grateful.

Rethink

During his trip to Wuzhishan, Lei Zhang kept thinking about the booking experience. She realized that this was not just a simple booking mistake, but a profound lesson about human nature, technology and rules. With the development of information technology, how to make technology closer to human nature has become the focus of her thinking.

Epilogue

At night, Lei Zhang sat at the top of Wuzhishan Mountain, looking at the starry sky, filled with emotion. This trip brought her not only beautiful scenery, but also profound thoughts on life, technology and humanity. She believes that in the future, people will be able to use technology more intelligently and make their lives better.

5.5.2 DIKWP and semantic mathematics in the in-depth interpretation of ticket booking cases

In an ordinary ticket booking, Lei Zhang's small mistake triggered a series of events. Through the application of DIKWP model and semantic mathematics, this report deeply analyzes this case, revealing the complexity of user experience in digital service platform and the potential ways to improve service quality.

Data in-depth analyses

Semantic analysis of the data: Lei Zhang booked the ticket through the online platform at 8:00 in the evening, but booked the ticket in the wrong direction by mistake because of the ambiguity of the interface design. Semantic mathematics reveals the key variables of user operation here: time, place selection and user expectation.

Misunderstanding and correction of data: the data display mode of the platform fails to effectively prevent user's operation errors, and the customer service lacks flexibility and user-friendliness in dealing with user's errors.

Information processing in detail

Deep interpretation of information: the application of semantic mathematics reveals the subjectivity of users in information interpretation and the lack of platform information transmission. This includes unclear interface design and mechanical customer service guidance.

Improve the efficiency of information interaction: the platform needs to optimize information transmission, such as instructions and warnings on the interface and interactive scripts of customer service, so as to make it more in line with users' semantic understanding.

Knowledge construction and application

Identification and filling of knowledge gap: The knowledge gap when users use the platform includes their ignorance of the refund process. The platform can use semantic mathematics to build a more effective user education mechanism.

Dissemination and sharing of knowledge: build knowledge base, such as operation guide and FAQ, to make it easier for users to understand and reduce misoperation.

Wisdom application and promotion

Practice of intelligent decision-making: Lei Zhang actively sought solutions after realizing his mistakes, showing the wisdom of problem solving. When formulating rules, the platform should consider the user experience and adopt a more humanized strategy.

The importance of ethical considerations in the application of wisdom: consider ethics and fairness in the formulation and implementation of rules to ensure the justice and rationality of user experience.

Purpose clarification and realisation

Identification and adaptation of user's purpose: Lei Zhang's original purpose was to order tickets conveniently, and the quick response and target adjustment when facing problems showed a clear goal consciousness.

Goal-oriented service optimization: service optimization needs to focus on the core needs of users, such as providing more intuitive operation processes and flexible refund options.

The analysis of this case shows the importance of DIKWP model and semantic mathematics in practical application. Through these tools, user experience can be effectively optimized, misunderstandings can be reduced and service quality can be improved. Lei Zhang's experience is not only an episode of a booking platform, but also a profound insight into the user experience in the field of digital services. In the future development, the combination of semantic mathematics and DIKWP model will play a key role in improving service quality and building a user-friendly system, and provide valuable insights and guidance for the development of digital services.

5.5.3 Solution: Optimize the ticket booking case by combining DIKWP and semantic mathematics.

In view of the case that Lei Zhang mistakenly booked a ticket on the ticket booking platform due to unclear interface design, this scheme aims to make a comprehensive optimization by using the principles of DIKWP model and semantic mathematics, so as to improve the user experience and reduce operational errors.

Data optimization scheme

Optimization of data presentation:

Optimize the interface design to make the choice of departure and destination more intuitive, for example, by color coding and icons.

A dynamic prompt is introduced, and when the user selects the train number, the detailed information of the selected train number is clearly displayed through a pop-up window or a sliding bar.

Error prevention mechanism:

Before booking, add a step to ask the user to confirm his choice, for example, "You have chosen the ticket from Wuzhishan to Haikou, are you sure?"

The design algorithm predicts and points out potential errors based on the user's historical booking data. For example, if the user usually starts from Haikou, the system can prompt to check whether the wrong starting point is selected.

Information processing scheme

Clarity of interface information:

The information on the interface should be concise, especially the explanation of the refund rules.

Explain the booking steps with diagrams and simple language to reduce the difficulty of understanding.

Optimization of customer service communication;

Provide professional training for customer service so that it can provide more specific and effective solutions.

Introduce automatic reply system to deal with common questions and improve efficiency.

Knowledge construction

User education and guide:

Provide detailed user guide and FAQ to educate users on how to operate the platform correctly.

Use multimedia tools such as video tutorials and diagrams to enhance the educational effect.

Knowledge sharing and feedback mechanism:

Create a user community to promote the exchange of experience and learning among users.

Set up feedback channels, collect user suggestions, and continuously improve the platform.

Wisdom application

Balancing rules and user experience:

Re-examine the refund rules and adjust them to a more flexible and user-friendly way.

Provide personalized solutions under special circumstances, such as temporary refund or change of service.

Intelligent decision support system;

Use data analysis and machine learning to predict user behavior and solve possible problems in advance.

Customize the personalized ticketing experience according to the user's historical behavior.

Purpose clarification

Identification and adaptation of user's purpose;

Identify the basic needs of users, such as convenient and fast booking.

Adjust the service according to the user's behavior and feedback to make it closer to the user's needs.

Goal-oriented service design;

Design service process centered on user satisfaction.

Evaluate the service effect regularly to ensure that it always meets the needs of users.

Through the comprehensive application of DIKWP model and semantic mathematics, we can deeply analyze the case of Lei Zhang and put forward effective optimization measures. These measures fundamentally solve the problems of information misreading and user misoperation, and greatly improve the user experience. In addition, the optimization scheme also emphasizes intelligent decision-making and goal-oriented service design, which helps the platform to better serve users in the long-term development.

5.5.4 Simulate the trial operation of the improved ticket booking system.

1. User access interface

User Lei Zhang visits the improved ticket booking platform.

The platform displays an intuitive interface design, and the departure and destination are clearly indicated by color coding and icons.

2. The user selects the ticket

Lei Zhang chooses the ticket from Haikou to Wuzhishan.

System dynamic prompt: "You chose the ticket from Haikou to Wuzhishan."

3 confirm the booking

The system prompts Lei Zhang to confirm her choice: "Your choice is to start from Haikou to Wuzhishan. Are you sure?"

Lei Zhang confirmed the choice.

4. Predict and correct mistakes

The system checks the historical booking data of Lei Zhang to confirm that the selection is correct.

System feedback: "Your choice seems ok."

5 complete the booking

Lei Zhang completed the ticket order, and the system showed: "The ticket order was successful."

6. Collect user feedback

The system provides feedback options, asking Lei Zhang whether he is satisfied with the service and collecting opinions: "Please leave your feedback to help us improve the service."

Through this simulation trial operation, we can see that the improved ticket booking system provides a more intuitive user interface, dynamic prompts, error prevention mechanism, and user feedback collection, which effectively improves the user experience and reduces the possibility of operational errors. This system design is more humanized and can better meet the needs of users.

5.5.5 Improved ticket booking system: simulation of refund process

1. The user accesses the refund interface.

User Lei Zhang needs to return the purchased ticket from Haikou to Wuzhishan for some reason.

She logged into the ticket booking platform and easily found the "My Order" page.

2. Select the refund option.

Lei Zhang finds the car ticket in her order and clicks the "Refund" button.

The system prompts: "Are you sure you want to return the ticket from Haikou to Wuzhishan?"

3. The system provides refund guidance.

After confirming the refund, the system will automatically display the refund process and possible refund fees.

The system displays: "According to your booking time, you can refund the ticket in full. Please click OK to continue. "

4. Refund confirmation

Lei Zhang clicks OK to refund the ticket.

The system immediately processes the refund request and displays: "Your ticket has been successfully unsubscribed, and the refund will be returned to your payment account within 1-3 working days."

5. User feedback collection

The system asks about the experience of Lei Zhang's refund process and provides a feedback form.

Lei Zhang filled in the feedback: "The refund process is simple and clear, and I am very satisfied."

6. Customer service support

Although the refund process is automated, the system still provides customer service contact information in case of emergency.

The system displays: "If you have any questions, please feel free to contact our customer service."

7. Complete the refund process

Lei Zhang quit the system and was satisfied with this relaxed refund experience.

Through this simulation trial operation, the improved ticket booking system shows more user-friendly and efficient characteristics in the refund process. The system provides clear guidance, fast processing and timely feedback collection mechanism, which ensures smooth and satisfactory user experience. This design embodies the effectiveness of semantic mathematics and DIKWP model in practical application, especially in improving user experience and simplifying complex processes.

This paper reveals the complexity of user experience in digital service platform and the potential ways to improve service quality through in-depth analysis of ticket booking cases. By using DIKWP model and semantic mathematics, the causes of user operation errors and the lack of platform services are identified, and a comprehensive optimization scheme is put forward accordingly. The improved ticket booking system and ticket refund process simulation show the effectiveness of the optimization measures and significantly improve the user experience. This research emphasizes how to use advanced theoretical tools and methods to improve service quality and make it more in line with users' expectations and needs in the information technology era. Our research results not only have direct application value to the ticket booking platform, but also provide reference experience and strategies for other digital service platforms. In the future, we expect more research to pay attention to and explore how to use scientific and technological means to enhance user experience and promote the development of digital services in a more humane and intelligent direction.

5.6 Semantic space interpretation of four-color theorem based on Existence Computation and semantic computation

In this chapter, a new method is proposed to explain the four-color theorem, which is regarded as a problem about EXCR and ESCR. We regard the plane as an existence space, the region is represented as a disjoint graph, and the color is represented as semantic discrimination. By analyzing the existing semantics and semantic range, we determine the minimum number of colors needed in different situations, and emphasize that semantic differentiation is carried out within a specific semantic range. We also discussed the situation when there are four or more lines, and pointed out how to determine the number of colors needed. The interpretation of this semantic space provides a new perspective for understanding the four-color theorem and an interesting method for solving similar graph theory and combination problems. By transforming the problem into the analysis of existential semantics and semantic scope, we can understand the demand of color quantity more clearly and provide a new direction for future research.

5.6.1 Semantic Interpretation of Four-color Theorem

The four-color theorem is a famous graph theory problem, and its semantic interpretation can be carried out through the framework of EXCR and ESCR. The goal of the four-color theorem is to find the combined examples of all regions on a plane, so as to determine how many colors are needed to distinguish these regions at least, so that there will be no same colors between adjacent regions. In this problem, we can regard the plane as a mathematical space, with regions represented as disjoint figures and colors represented as distinguishing semantics. In order to explain the semantics of regions on the plane, we can use existential semantic analysis, in which the existential semantics of points, lines and planes can help us understand the relationship between these regions.

First, let's define the meaning of color in existential semantics. Color C can be regarded as a kind of distinguishing semantics (SM), which is used to distinguish the filled area CZ from the unfilled area NZ. We can express it as:

SM(C) := (CZ, NZ)

Here, CZ represents the filled area and NZ represents the unfilled area. For the four-color theorem, our goal is to find the minimum number of colors so that all regions can be correctly distinguished.

5.6.2 Semantic analysis of existence in different situations

Examples of areas with no essential line definition

When there are no lines on the plane, that is, there are no divided areas, we only need one color c1 to fill the whole plane. This is because there is no divided area, so only one color is enough. This can be expressed as:

NUM(Z) = 0 NUM(SMD0 (C)) = NUM(SMD0 (c1)) = 1

From the perspective of existential semantics, NUM(SMD0 ({c1})) means that there is no need for at most one kind of basic area marker existential semantics when there are no lines on the plane.

Examples of an area defined by an essentially existing line

When there is a line l1 on the plane, we can divide the plane into two regions Z1 and Z2. These two areas need to be marked with different colors. We can use two colors c1 and c2 to mark these two areas. This can be expressed as:

NUM({Z1, Z2}) = 2 NUM(SMD1 (C)) = NUM(SMD1 ({c1, c2})) = 2

From the perspective of existential semantics, NUM(SMD1 ({c1, c2})) means that there are at most two basic existential semantics when there is a line on the plane.

Examples of areas defined by two essentially existing lines

When there are two lines l1 and l2 on the plane, these two lines further divide the region into four parts, namely, Z11, Z12, Z21 and Z22. These four areas need to be marked with different colors. We can use four colors c1, c2, c3 and c4 to mark these four areas. However, it should be noted here that the four regions are distinguished within a certain semantic range, specifically, they are distinguished within the semantic range of ASS(R(rx), R(ry)), where R(rx) and R(ry) respectively represent the values of horizontal and vertical coordinates. This means that the distinction between these four areas is completely within the scope of these two coordinates. Essentially, this is a specific semantics.

NUM(SMD2, SMD1) = NUM(SMD2 ({c1, c2, c3, c4})) = 4

From the perspective of existential semantics, NUM(SMD2 ({c1, c2, c3, c4}) means that there are at most four basic existential semantics when there are two lines on the plane.

Examples of areas defined by three essentially existing lines

When there are three lines l1, l2 and l3 on the plane, these lines further divide the region and produce more regions. In this case, we need to consider the existence of line l3. Line l3 corresponds to the example of ASS(X, Y), which further divides the existing area into more parts. These new areas will need more colors to mark, but we can still determine the number of colors needed through the analysis of existential semantics.

It should be noted here that the distinction between new regions is within a new semantic range, namely, ASS(R(rx), R(ry), ASS(R(rx), R(ry)). This means that the distinction between new areas is completely within the scope of these two coordinates and the new semantic scope. From the perspective of existential semantics, we can continue to apply the same method to determine the number of colors needed.

NUM(SMD3 ({c1, c2, c3, c4})) = 4

From the perspective of existential semantics, NUM(SMD3 ({c1, c2, c3, c4}) means that there are at most four basic existential semantics when there are three lines on the plane.

Examples of areas defined by four or more lines

When there are four or more lines on the plane, we can continue to apply the same method to determine the number of colors needed. Specifically, if the newly introduced line is parallel to the existing line set, no new color requirements will be introduced, otherwise, we can determine the required number of colors according to Theorem (rZCO) or Theorem (rZCI).

To sum up, we use the framework of EXCR and ESCR to explain the semantics of the four-color theorem. Under this framework, we regard the plane as an existence space, the region is represented as disjoint graphics, and the color is represented as semantic discrimination. By analyzing the semantic meaning and semantic range, we can determine the minimum number of colors needed in a given situation.

We also discussed the semantic analysis of existence in different situations, including the situation that there is no line, one line, two lines and three lines. In each case, we have determined the number of colors needed and explained the semantic relationship between these colors. In particular, we emphasize that distinguishing semantics is carried out within a specific semantic range, which is very important for understanding the semantic interpretation of the four-color theorem.

Finally, we also discuss the situation when there are four or more lines, and point out how to determine the number of colors needed in these situations. Generally speaking, by transforming the four-color theorem into the problem of Existence Computation and semantic computation, we can understand this problem more clearly and determine the minimum number of colors needed for the solution.

The interpretation of this semantic space provides a new perspective, which can help us better understand the four-color theorem and provide an interesting method to solve this classic problem. By transforming the problem into the analysis of existential semantics and semantic scope, we can understand the demand of color quantity more clearly and provide a new direction for future research. This method can also be applied to other similar graph theory and combinatorial problems, which provides a powerful tool for us to understand and solve these problems.

5.7 Semantic Interpretation and Semantic Space Construction of Goldbach's Conjecture Based on Existence Computation and Semantic Computing

This chapter aims to reinterpret the famous number theory problem Goldbach's Conjecture, and explore a new perspective of this problem by constructing semantic space. Goldbach's Conjecture claims that every even number greater than 2 can be expressed as the sum of two prime numbers. However, although this problem has aroused widespread concern in the field of mathematics, a general solution has not yet been found. In this paper, we reinterpret Goldbach's Conjecture as a question about EXCR and ESCR, and analyze the importance of existential semantics and semantic scope. We analyze Goldbach's Conjecture from the semantic perspective of type examples and overall types, and emphasize the key concepts of semantic existence and semantic scope. By establishing semantic associations with natural number types and prime number types, we get the type semantics of Goldbach's conjugate. Finally, we discuss the potential influence of this new interpretation method on mathematical research and its application prospect in solving similar graph theory and combinatorial problems. This research provides a new theoretical framework for Goldbach's Congress and a new direction for future research.

5.7.1 Semantic interpretation of Goldbach's Conjecture

Goldbach's Conjunction is a famous number theory problem, which claims that every even number greater than 2 can be expressed as the sum of two prime numbers. Although this problem has aroused widespread interest in mathematics, a general solution has not yet been found. In this paper, we reinterpret Goldbach's Conjecture as a question about EXCR and ESCR, and explore this classic question by constructing a semantic space (SCR).

Semantic perspective of examples of semantic interpretation types of Goldbach's Conjecture.

First of all, we explain it from the semantic point of view of examples of types of Goldbach's Conjecture. Suppose we have an even number e, which is an instance of Goldbach's Conjunction. We can use the instantiation relation INS(E) to represent the instance of e, where e represents the specific value of e. According to Goldbach's Conjunction, we can express e as the sum of two prime numbers p, that is, E = P1+P2.

This can be expressed in the following ways:

INS(E) := ASS((INS(P), INS(P)), REL(+))

Here, INS(P) represents an example of prime number p, and p1 and p2 are specific values of p respectively. Through the relation REL(+), we can add two instances of prime numbers, and thus get an instance of e.

Semantic angle of overall type

Next, we will consider the semantic interpretation of Goldbach's Conjunction from the semantic perspective of the whole type. For any instance of even number E, we can equivalently deduce the corresponding semantic association at the type level through the cross-type instance level and according to the basic assumption axiom of Existence Computation and Reasoning Excr, that is, the Conservation of Existence Set (CEX).

In this case, even type E can establish an existential semantic association with natural number type Z through type level semantic association E(x) := R(y)+R(y), because E can be expressed as the sum of two natural numbers. Here, R(y) represents the existential semantics of the natural number y.

This can be expressed in the following ways:

INS(E) := ASS((INS(Z), INS(Z), REL(+)))

Here, INS(Z) represents an example of the natural number z. Through the relation REL(+), we can add two instances of natural numbers to get an instance of e.

Similarly, we can express the semantic association at the type level between prime number type P and natural number type Z, because prime number P cannot be divisible by other natural numbers except 1 and itself, which can be expressed as P = ASS(Z,! ())。 Here! () indicates a semantic relationship that is not equal to (*), that is, it cannot be multiplied by other natural numbers.

On the basis of confirming that there is semantics at the type level of prime number P, we can use the basic hypothesis axiom of Essence Computation and Reasoning Escr, that is, the CES (Consistency of Compounded Essential Set). According to the concrete semantic relationship of the semantic relationship ASS(P, Z) between the instance INS(P) of prime number P and the instance INS(Z) of natural number type Z, the corresponding semantic relationship at the type level is established through the mapping between the type and the instance level. Finally, the type semantics of Goldbach's Conjecture P+P = E is obtained.

5.7.2 Supplementary explanation

It should be noted that for the semantics of composite number c, we can't simply establish semantic association at the type level as the number of pixels. Because the type of composite number is not the essential semantic existence, the semantic definition of composite number itself involves the semantic existence of higher level types. The existential dependence of semantics cannot be established in reverse, which is a supplement to the axiom of conservation of existence (CEX) and the axiom of combinatorial consistency of global integrity of essential sets (CES).

Through the methods based on EXCR and ESCR, we reinterpret Goldbach's society and construct a semantic space (SCR) to study this number theory problem deeply. We analyze Goldbach's Conjecture from the semantic point of view of type examples and overall types, and emphasize the importance of semantic existence and semantic scope. This new explanation provides us with a different perspective, enables us to understand Goldbach's Congress more clearly, and provides a new direction for future research.

By transforming the problem into the analysis of existential semantics and semantic scope, we can discuss mathematical problems more deeply, and at the same time provide an interesting method for solving similar graph theory and combinatorial problems. As a classical problem of number theory, Goldbach's Conjecture has always attracted the interest of mathematicians. We hope that this new interpretation method can provide more inspiration for solving this problem, and also provide a new tool and perspective for mathematical research.

5.8 Collatz Conjecture's Semantic Explanation and Semantic Space Exploration

Collatz Conjecture, or 3x+1 problem, is a challenging problem in the fields of mathematics and computer science. The expression of this problem is relatively simple: for any positive integer n, if n is an even number, divide it by 2; If n is odd, multiply it by 3 and add 1. After several iterations, eventually n will become 1. The core question of Collatz Conjecture is whether this process can always converge to 1 for any positive integer n.

Although the problem of Collatz Conjecture is simple, so far, a general method has not been found to prove whether it is true or not. This problem has puzzled mathematicians and computer scientists for decades. Although numerical calculation shows that it still holds true for a huge value of n, it has not yet found a universally applicable proof.

The purpose of this technical report is to provide a brand-new explanation and in-depth exploration of Collatz Conjecture by introducing the frameworks of Existence Computation and Reasoning (EXCR) and Essence Computation and Reasoning (ESCR) and building a semantic space (SCR). We will examine this problem from different angles, thus providing new ideas and tools for solving it.

5.8.1 Semantic Explanation of Collatz Conjecture

Semantic angle of type instance

First of all, we will explain this problem from the semantic point of view of an instance of the type of CollatzConjunction. Suppose we have a natural number n, which is an instance of Collatz Conjecture. We can use the instantiation relation INS(N) to represent an instance of n, where n represents a specific numerical value of n. According to Collatz Conjecture, for any instance of natural number n, it is either an odd O instance (INS(O)=o) or an even E instance (INS(E)=e). According to the rules of Collatz Conjecture, if n is an odd O, multiply it by 3 and add 1, that is, n: = 3o+1; If n is an even number e, divide it by 2, that is, n:=e/2. Repeat this operation, and finally you can get n=1.

This can be expressed in the following ways:

scssCopy codeINS(N) := ASS({INS(O), INS(E)}, {REL(+), REL(/)})

Here, INS(O) and INS(E) represent examples of odd o and even e respectively, and o and e are their specific values respectively. By the relationship between REL(+) and REL(/), we can express the operations of multiplying by 3, adding 1 and dividing by 2. Finally, through this series of operations, we get n=1, which is the termination condition of Collatz Conjecture.

Semantic perspective of the overall type

Next, we will consider the explanation of Collatz Conjecture from the semantic point of view of the overall type of the instance. For any instance of natural number n, we can equivalently deduce the corresponding semantic association at the type level through the semantic association at the instance level of odd O or even E across types, according to the basic assumption axiom of EXCR, that is, the Conservation of Existence Set, CEX).

In this case, odd type O or even type E can be related to each other through semantic association N(E):=N(O)+1 at the type level. This is because the relationship between odd and even numbers is established by N(E):=N(O)+1.

scssCopy codeASS(TYPE(O), TYPE(E)) := ASS((TYPE(O), TYPE(E)), {REL(+), REL(/)})

Type-level semantic association N(E):=N(O)+1 implies the semantic equivalence between odd type O and even type E.

scssCopy codeN(E) := N(O) + 1 => EXCR(TYPE(O)) := EXCR(TYPE(E)) => EXCR(O) := EXCR(E)

From this, we can rely on the conservation axiom CEX of EXCR's basic hypothesis axiom to determine the semantic equivalence of the existence between natural number type N and odd number type O and even number type E.

5.8.2 Construction and exploration of semantic space

When discussing the semantic Explanation of Collatz Conjecture, we not only consider the semantics of a single instance, but also pay attention to the construction and exploration of Semantic Space, SCR). Semantic space is a conceptual space, which contains all semantic information related to Collatz Conjecture, thus helping us to understand the essence of the problem more deeply.

In the semantic space, we can regard each natural number n as a node, and the nodes are connected with each other through different semantic associations. These semantic associations include semantic associations of instances, types and overall types. These associations describe the relationship between natural numbers and their transformation rules in Collatz Conjecture.

Semantic association of examples

In the semantic space, each natural number n has its corresponding instantiation relation INS(N), which describes whether it is odd or even, and performs corresponding operations according to the rules of Collatz Association. The semantic association of these examples forms an important part in the semantic space, which helps us to understand the behavior of a single natural number n. Through continuous iteration, the relationship between examples constitutes a complex semantic network, which reflects the transformation logic between values.

Semantic association of types

In addition to the semantic association of instances, the semantic association of types also plays a key role in semantic space. By associating the instance type of natural number n with the instance types of odd type O and even type E, we establish a higher level semantic relationship. These relations show the conversion rules between odd and even numbers, and explain why the operation of Collatz Conjecture can finally converge to 1. This high-level relationship embodies the mathematical logic of Collatz Conjecture operation and provides us with a deeper understanding.

Semantic association of overall types

Finally, the semantic association of the whole type provides a global perspective. By connecting the whole of different numerical types, we get a more comprehensive understanding of collatz conjugate. This global perspective reveals the semantic unity of different numerical types in the Collatz process. This unity emphasizes the internal consistency of the whole problem, which helps us to understand the Collatz Conjecture more clearly.

5.8.3 The importance of bounded semantics

There is a key concept in the semantic Explanation of Collatz Conjecture, namely bounded semantics. Bounded semantics means that the processing of a problem will not lead to an infinite increase or decrease in numerical value, but will eventually tend to a limited range of values. This concept is very important in the research of Collatz Conjecture, because it is directly related to the solution of the problem.

The definition of bounded semantics enables us to better understand why Collatz Convergence converges to 1 at a certain point without infinite loop or infinite increase. It helps us to determine the key feature of the problem, which is widely used in mathematical research and calculation theory. The concept of bounded semantics makes the solution of Collatz Conjecture more clear and feasible.

By introducing the framework of EXCR and ESCR, this technical report provides a new explanation and in-depth exploration for Collatz Conjecture. This method not only helps to solve specific mathematical problems, but also provides new ideas and tools for mathematical research.

Contribution of Semantic Space

The construction of semantic space provides a comprehensive perspective for understanding and solving Collatz Conjecture. It presents the problem from different angles, which enables us to explore the essence of Collatz Conjecture more deeply. Through the semantic space, we can more clearly understand the examples of natural number n, the relationship between types and the consistency of the overall types. This comprehensive perspective is helpful for us to understand the Collatz Conjecture more comprehensively.

The practicability of bounded semantics

The concept of bounded semantics has important practicability in the research of Collatz Conjecture. It not only helps us to understand why the collatz convergence converges to 1 at a certain point, but also provides us with clues to solve this problem. Bounded semantics makes the problem clearer and helps us to promote the final solution of the problem. This concept can also be applied in other mathematical problems and calculation theories, which provides a new way of thinking for mathematical research.

The new direction of mathematical research

The methods and tools proposed in this technical report provide new ideas for exploring a wider range of mathematical problems. By transforming the problem into the analysis of existential computation and semantic computation, we can explore the essence of mathematical problems more deeply. Collatz Conjecture, as a concerned problem of number theory, has always attracted the interest of mathematicians. We hope that this new Explanation method can provide more inspiration for solving this problem, and also provide a new tool and perspective for mathematical research.

Throughout the discussion, we emphasized the basic assumptions of EXCR and ESCR, such as the conservation axiom of existence (CEX), the combinatorial consistency axiom of global integrity of essential sets (CES) and the semantic inheritance axiom of existence (IHES). These basic assumptions build a solid semantic foundation for us, which enables us to deeply explore the semantic essence of Collatz Conjecture.

By transforming the problem into the analysis of existential computation and semantic computation, we can not only solve the concrete problem of Collatz Conjecture, but also provide new research directions and methods for other problems in the field of mathematical research and computational theory. The following are some possible prospects and future research directions:

Wider research on mathematical problems: We can apply the framework of existential computing and semantic computing to study other mathematical problems. These problems may include various unsolved problems in the fields of number theory, graph theory and set theory. This new method may help us to better understand and solve complex mathematical problems.

Application of computer science: the framework of existential computing and semantic computing is not only applicable to the field of mathematics, but also to problems in computer science. For example, in the theory of algorithm design and computational complexity, this framework may be helpful to analyze the performance and efficiency of the algorithm and find a better algorithm solution.

Machine learning and artificial intelligence: Introducing the ideas of existential computing and semantic computing into the fields of machine learning and artificial intelligence may help to develop more intelligent algorithms and systems. This can include applications in automatic reasoning, knowledge representation and autonomous decision-making.

Mathematics education: Introducing this new explanation method into mathematics education can help students better understand the essence and logic of mathematics problems. This is helpful to train mathematicians and computational scientists with more creative and critical thinking ability.

Interdisciplinary research: the framework of existential computing and semantic computing has interdisciplinary characteristics, which can promote cooperation and exchanges in different fields. Mathematicians, computer scientists, philosophers and researchers in other fields can discuss the application and potential of this framework in their respective fields.

In a word, the semantic Explanation of Collatz Conjecture and the exploration of semantic space provide us with a brand-new way to think about mathematical problems. By transforming the problem into the context of existential computing and semantic computing, we can understand the essence of the problem more deeply and look for clues to the solution. This method is not only valuable for solving specific problems, but also provides a new direction and tool for mathematical research and exploration in other fields. In the future, we can expect to see more research work on existential computing and semantic computing, and the wide application of this framework in different fields.

6 Conclusion

This paper conducts in-depth research and discussion around the cutting-edge theoretical framework of semantic mathematics and its application in the DIKWP model. Firstly, semantic mathematics successfully realizes the transformation from abstract concepts to concrete semantic space by innovatively reconstructing conceptual relationships and logical structures, thus giving deeper meanings to data, information, and knowledge, and being able to accurately capture and realize purpose in complex contexts.

In combining the application analysis of the DIKWP model, this paper elaborates in detail how semantic mathematics can promote the semantic reconstruction of data, the deep processing of information, the construction of knowledge systems, and the effective and comprehensive use of wisdom, and reveals the core roles of Essence Computation and Reasoning, Existence Computation and Reasoning, and Purpose Computation and Reasoning in the model. Through a series of examples, such as medical diagnosis, financial risk management, scientific research, project decision-making, etc., the paper demonstrates the power and universality of the combination of semantic mathematics and the DIKWP model in solving practical problems. The paper further explores the far-reaching perspectives of semantic mathematics on science and technology, especially in the areas of semantic understanding of integers, new interpretations of mathematical operations, and innovations in mathematical logic and reasoning frameworks. By analyzing the semantic connotations of classical mathematical problems such as Goldbach's conjecture and the four-color theorem, the paper not only enriches our understanding of the nature of mathematics, but also reveals the unique value of semantic mathematics in promoting the progress of scientific research.

Finally, the paper prospectively discusses the potential of semantic mathematics for a wide range of applications in various fields of today's information-based society, especially the key role it plays in the construction of the digital economy of the Hainan Free Trade Port, as well as a new perspective on the interpretation of traditional philosophy and culture. Through the deep interdisciplinary integration and practical application cases, the paper fully proves the important position and broad perspective of semantic mathematics as a new type of mathematical tool in solving real problems, driving scientific and technological innovation and promoting social development.

Reference

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Data can be regarded as a concrete manifestation of the same semantics in our cognition. Often, Data represents the semantic confirmation of the existence of a specific fact or observation, and is recognised as the same object or concept by corresponding to some of the same semantic correspondences contained in the existential nature of the cognitive subject's pre-existing cognitive objects. When dealing with data, we often seek and extract the particular identical semantics that labels that data, and then unify them as an identical concept based on the corresponding identical semantics. For example, when we see a flock of sheep, although each sheep may be slightly different in terms of size, colour, gender, etc., we will classify them into the concept of "sheep" because they share our semantic understanding of the concept of "sheep". The same semantics can be specific, for example, when identifying an arm, we can confirm that a silicone arm is an arm based on the same semantics as a human arm, such as the same number of fingers, the same colour, the same arm shape, etc., or we can determine that the silicone arm is not an arm because it doesn't have the same semantics as a real arm, which is defined by the definition of "can be rotated". It is also possible to determine that the silicone arm is not an arm because it does not have the same semantics as a real arm, such as "rotatable".

Information, on the other hand, corresponds to the expression of different semantics in cognition. Typically, Information refers to the creation of new semantic associations by linking cognitive DIKWP objects with data, information, knowledge, wisdom, or purposes already cognised by the cognising subject through a specific purpose. When processing information, we identify the differences in the DIKWP objects they are cognised with, corresponding to different semantics, and classify the information according to the input data, information, knowledge, wisdom or purpose. For example, in a car park, although all cars can be classified under the notion of 'car', each car's parking location, time of parking, wear and tear, owner, functionality, payment history and experience all represent different semantics in the information. The different semantics of the information are often present in the cognition of the cognitive subject and are often not explicitly expressed. For example, a depressed person may use the term "depressed" to express the decline of his current mood relative to his previous mood, but this "depressed" is not the same as the corresponding information because its contrasting state is not the same as the corresponding information. However, the corresponding information cannot be objectively perceived by the listener because the contrasting state is not known to the listener, and thus becomes the patient's own subjective cognitive information.

Knowledge corresponds to the complete semantics in cognition. Knowledge is the understanding and explanation of the world acquired through observation and learning. In processing knowledge, we abstract at least one concept or schema that corresponds to a complete semantics through observation and learning. For example, we learn that all swans are white through observation, which is a complete knowledge of the concept "all swans are white" that we have gathered through a large amount of information.

Wisdom corresponds to information in the perspective of ethics, social morality, human nature, etc., a kind of extreme values from the culture, human social groups relative to the current era fixed or individual cognitive values. When dealing with Wisdom, we integrate this data, information, knowledge, and wisdom and use them to guide decision-making. For example, when faced with a decision-making problem, we integrate various perspectives such as ethics, morality, and feasibility, not just technology or efficiency.

Purpose can be viewed as a dichotomy (input, output), where both input and output are elements of data, information, knowledge, wisdom, or purpose. Purpose represents our understanding of a phenomenon or problem (input) and the goal we wish to achieve by processing and solving that phenomenon or problem (output). When processing purposes, the AI system processes the inputs according to its predefined goals (outputs), and gradually brings the outputs closer to the predefined goals by learning and adapting.

Yucong Duan, male, currently serves as a member of the Academic Committee of the School of Computer Science and Technology at Hainan University. He is a professor and doctoral supervisor and is one of the first batch of talents selected into the South China Sea Masters Program of Hainan Province and the leading talents in Hainan Province. He graduated from the Software Research Institute of the Chinese Academy of Sciences in 2006, and has successively worked and visited Tsinghua University, Capital Medical University, POSCO University of Technology in South Korea, National Academy of Sciences of France, Charles University in Prague, Czech Republic, Milan Bicka University in Italy, Missouri State University in the United States, etc. He is currently a member of the Academic Committee of the School of Computer Science and Technology at Hainan University and he is the leader of the DIKWP (Data, Information, Knowledge, Wisdom, Purpose) Innovation Team at Hainan University, Distinguished Researcher at Chongqing Police College, Leader of Hainan Provincial Committee's "Double Hundred Talent" Team, Vice President of Hainan Invention Association, Vice President of Hainan Intellectual Property Association, Vice President of Hainan Low Carbon Economy Development Promotion Association, Vice President of Hainan Agricultural Products Processing Enterprises Association, Visiting Fellow, Central Michigan University, Member of the Doctoral Steering Committee of the University of Modena. Since being introduced to Hainan University as a D-class talent in 2012, He has published over 260 papers, included more than 120 SCI citations, and 11 ESI citations, with a citation count of over 4300. He has designed 241 serialized Chinese national and international invention patents (including 15 PCT invention patents) for multiple industries and fields and has been granted 85 Chinese national and international invention patents as the first inventor. Received the third prize for Wu Wenjun's artificial intelligence technology invention in 2020; In 2021, as the Chairman of the Program Committee, independently initiated the first International Conference on Data, Information, Knowledge and Wisdom - IEEE DIKW 2021; Served as the Chairman of the IEEE DIKW 2022 Conference Steering Committee in 2022; Served as the Chairman of the IEEE DIKW 2023 Conference in 2023. He was named the most beautiful technology worker in Hainan Province in 2022 (and was promoted nationwide); In 2022 and 2023, he was consecutively selected for the "Lifetime Scientific Influence Ranking" of the top 2% of global scientists released by Stanford University in the United States. Participated in the development of 2 international standards for IEEE financial knowledge graph and 4 industry knowledge graph standards. Initiated and co hosted the first International Congress on Artificial Consciousness (AC2023) in 2023.

Prof. Yucong Duan

DIKWP-AC Artificial Consciousness Laboratory

AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory

DIKWP research group, Hainan University

duanyucong@hotmail.com

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