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Definition of Data, Information and Knowledge in DIKWP Model
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Definition of Data, Information and Knowledge in DIKWP Model
Yucong Duan
Benefactor: Shiming Gong
AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory
DIKWP-AC Artificial Consciousness Standardization Committee
World Conference on Artificial Consciousness
World Artificial Consciousness Association
(Email：duanyucong@hotmail.com)
Catalog
4.1 DIKW model (data-information-knowledge-wisdom)
4.2 SECI model (socialization, externalization, combination, internalization)
4.3 Polanyi's tacit knowledge theory
5 Case 5: Study on Plant Growth in Biology
6 Structured representation of knowledge
7 Cognition and construction of knowledge
8 Specific Case: Study on Planetary Motion in Astronomy
8.4 Structured representation of knowledge
9 The philosophical significance of knowledge
10 Dynamic verification and correction of knowledge
11 Conclusion of comprehensive technical report
12.1 DIKW model (data-information-knowledge-wisdom)
12.2 SECI model (socialization, externalization, combination, internalization)
12.3 Polanyi's tacit knowledge theory
12.5.2 Hypothesis and Abstraction
12.5.4 Systematic understanding
12.5.5 Dynamic verification and correction
12.5.6 Sharing and dissemination.
13 Comparison with other models
13.4 Overall Comparative Analysis of Professor Yucong Duan's Data-Information-Knowledge
14 Comparative analysis of data, information and knowledge in different spaces
14.1 Professor Yucong Duan's DIKWP model: the definition of three spaces
14.1.2 Semantic Space (semantic space)
14.1.3 Cognitive Space (cognitive space)
14.2 Comparative Analysis Table of Data, Information and Knowledge in Different Spaces
14.2.1 Comparative Analysis of Data
14.2.2 Comparative Analysis of Information
14.2.3 Comparative analysis of Knowledge
14.2.4 Overall Comparative Analysis of Data-Information-Knowledge
14.3 Comparison of data, information and knowledge with other models in different spaces
14.3.1 Comparative Analysis of Conceptual Space
14.3.2 Comparative Analysis of Semantic Space
14.3.3 Comparative Analysis of Cognitive Space
The concept of data In the DIKWP model, data is not only original and unprocessed facts and observation records. The concept of data is not only a simple fact or observation record, but also a cognitive object confirmed by the cognitive subject through the concept space in the cognitive process. This means that data have been classified and organized in some way before they are recognized, which makes them have a specific meaning in the conceptual space.
Semantics of data The semantics of data is a concrete manifestation of expressing the same meaning in the cognitive process. In cognitive space, data is not only a simple record, but also the result of semantic matching and probability confirmation of the cognitive objects corresponding to these data records through the classification and correspondence of conceptual space or semantic space. This processing method highlights the cognitive attribute of data in communication and thinking, that is, the meaning of data is identified and conceptually confirmed by concrete semantics through the comparison between cognitive subject and existing concepts and semantics.
For example, in a biological experiment, scientists collected growth data of various plants, including height, number of leaves, color and so on. These data are raw records in the initial stage. However, when these data are sorted, classified and input into a growth model, they are not only numerical or textual records, but also concrete expressions of plant growth. This process of classification and arrangement is the process of transforming original data into data with specific semantics.
The concept of information In the DIKWP model, information corresponds to one or more "different" semantics in the cognitive process. Information is to semantically associate the cognitive object of DIKWP in the cognitive space of the cognitive subject with the cognitive object of DIKWP in the semantic space through a specific purpose. Through this association, cognitive subjects can identify the differences in cognitive contents such as data, information, knowledge, wisdom or purpose. Through probabilistic confirmation or logical judgment, this difference forms information semantics in semantic space, or produces new semantic association in semantic space.
Semantics of information The semantics of information is to connect the cognitive content of data, information, knowledge, wisdom or purpose with the existing cognitive objects of cognitive subjects through a specific purpose, thus generating new semantic associations. In the cognitive space, this process not only includes the re-semantic combination and semantic transformation of the known DIKWP content, but also involves the generation of new cognitive understanding and information semantics through this re-combination and transformation. This process of semantic generation emphasizes the dynamic and constructive nature of information.
For example, in the above biological experiment, by analyzing and processing the growth data of plants, scientists can identify some patterns, such as the growth differences of different plants under different environmental conditions. These models are not only a simple display of data, but also contain scientists' cognitive purpose, that is, to understand and explain the internal mechanism of plant growth. Through this information processing process, scientists can transform the specific data of plant growth into information with explanatory and predictive ability.
The concept of knowledge In the DIKWP model, knowledge corresponds to one or more "complete" semantics in the cognitive space. Knowledge is to abstract the semantic integrity of DIKWP content by cognitive subjects with the help of certain assumptions, and to obtain the semantic understanding and explanation between DIKWP content of cognitive objects. This kind of understanding and explanation forms the semantic connection between the cognitive input DIKWP content of cognitive interaction activities and the existing cognitive DIKWP content, and corresponds to one or more "complete" semantics bearing complete cognitive confirmation in a higher-order cognitive space.
Semantics of knowledge The semantics of knowledge is to give "complete" semantics to some observation results through high-level cognitive activities, thus forming systematic understanding and rules. For example, it is impossible to know that all swans are white through observation, but in the cognitive space, cognitive subjects can give "complete" semantics to some observation results through assumptions (high-order cognitive activities that give complete semantics), that is, "all", and then form knowledge semantics corresponding to the knowledge rule that "swans are all white" with complete semantics.
In the case of biological experiments, scientists may find that some plants grow faster under certain conditions through long-term observation and experiments. By abstracting and assuming these observations, scientists can form the knowledge that "in high humidity environment, plant X grows faster". This kind of knowledge is not only the understanding of single information, but also the systematic understanding formed after repeated verification and correction, which can explain and predict the growth behavior of plants under different environmental conditions.
4 Comparative analysis of Professor Yucong Duan's DIKWP model with other knowledge definitions and models.
4.1 DIKW model (data-information-knowledge-wisdom)
Data: Original and unprocessed facts and observation records.
Information: data that has been processed and understood and given a specific meaning.
Knowledge: Further explanation and understanding of information, forming patterns and rules.
Wisdom: Use knowledge to make effective decisions and actions.
Professor Yucong Duan's definition emphasizes the dynamic generation and semantic integrity of knowledge, while DIKW model pays more attention to the hierarchical and static storage of knowledge.
Professor Duan's definition is oriented to the development from artificial intelligence to artificial consistence system, and pays attention to the high-order cognitive activities of cognitive subjects and the hypothetical generation of knowledge, while DIKW model mainly focuses on the transformation process from information to knowledge.
Professor Duan's definition emphasizes the abstraction and verification process of knowledge, while DIKW model mainly focuses on the classification and hierarchical structure of knowledge.
4.2 SECI model (socialization, externalization, combination, internalization)
Explicit knowledge: it can be transmitted by writing and coding.
Tacit knowledge: personal experience and insight, which are difficult to transmit directly.
Four processes: socialization, externalization, combination and internalization.
Professor Yucong Duan's definition emphasizes the semantic network and semantic integrity of knowledge, while SECI model pays more attention to the process of knowledge transformation and sharing.
SECI model emphasizes the interaction between explicit and implicit knowledge, while Professor Duan's definition focuses on the abstraction and verification of knowledge.
Professor Duan's definition is more suitable to describe the generation process of knowledge, while SECI model is more suitable to describe the flow and sharing of knowledge within the organization.
4.3 Polanyi's tacit knowledge theory
Explicit knowledge: it can be formalized and transmitted.
Tacit knowledge: personal experience and skills that are difficult to formalize and transmit.
Professor Yucong Duan's definition pays more attention to the semantic integrity and abstract process of knowledge, while Polanyi's theory emphasizes the importance and difficulty of transferring tacit knowledge.
Polanyi's theory pays more attention to the individuality of knowledge, and Professor Duan's definition also considers the sharing and dissemination of knowledge at the social level.
Professor Duan's definition provides a structured representation of knowledge, while Polanyi's theory emphasizes the individual experience and skills of tacit knowledge.
Five domains: simple, complex, complex, chaotic and disorderly, and the application and decision-making methods of knowledge in each domain are different.
Professor Yucong Duan's definition emphasizes the generation, verification and structuring of knowledge, while the Cynefin framework focuses on the application and decision-making of knowledge in different situations.
The framework of Cynefin is suitable for the decision-making of complex systems and problems, while Professor Duan's definition is suitable for the theoretical construction and semantic understanding of knowledge.
Professor Duan's definition provides the semantic network representation of knowledge, while the Cynefin framework provides the situational classification of knowledge application.
5 Case 5: Study on Plant Growth in Biology
Observation records: plant height, number of leaves, color, humidity, temperature, etc.
Data processing: record these raw data, classify and sort them out.
By analyzing and processing the data, the patterns and laws of plant growth are identified.
For example, identify the growth differences of plants under different humidity conditions.
Through long-term observation and experiment, a systematic understanding of plant growth law is formed.
Hypothetical formation: in high humidity environment, plant X grows faster.
Semantic integrity abstraction: abstract this observation result to form systematic knowledge.
Semantic connection construction: connecting new knowledge with existing plant growth theories.
Verification and correction: through further observation and experiment, verify and correct knowledge.
6 Structured representation of knowledge
The structural representation of knowledge emphasizes the systematicness of knowledge in cognition. In the DIKWP model, knowledge can be expressed as a semantic network:
K=(N,E)
Among them:
N represents a set of concepts.
E represents a set of relationships between concepts.
This kind of representation positions knowledge cognition as a higher cognitive achievement, emphasizing the structure of knowledge and the ability to capture complete semantics. This is very important for understanding complex systems and abstract concepts.
7 Cognition and construction of knowledge
Knowledge is the bridge of the cognitive state transformation from never understanding to understanding of DIKWP content, and the confirmation of knowledge is strengthened through verification. The construction of knowledge not only depends on the accumulation of data and information, but more importantly, it forms an understanding of the essence and internal relations of things through abstraction and generalization in the cognitive process. Knowledge exists not only at the individual level, but also at the collective or social level, and is shared and disseminated through culture, education and inheritance.
Knowledge semantics is a structured knowledge formed after deep processing and internalization of DIKWP content. This understanding is in the conceptual space and corresponds to the semantic space with the help of "complete" semantics. The definition of knowledge in the framework of DIKWP reflects a deep understanding of the world and a complete grasp of semantics. This echoes Aristotle's concept of formal reasons, that is, the essence and purpose of things can be explored and understood through reason and experience.
The formation of each knowledge rule represents the cognitive subject's cognitive grasp of the internal laws and essence of things in the DIKWP model. From the philosophical point of view, knowledge is not only the product of cognitive process, but also the purpose and guidance of this process. The formation and application of knowledge reflects the adaptation and transformation of the cognitive subject to the world, and it is a semantic space understanding of the deep-seated laws of the real world.
8 Specific Case: Study on Planetary Motion in Astronomy
We take the study of planetary motion in astronomy as an example to explain the concept and semantic generation process of knowledge in detail.
Observation records include:
The position of the planet (longitude and latitude)
The trajectory of the planet (recorded by telescopes and photographic equipment)
Time record (observation time and date)
Distance between celestial bodies (using radar ranging and other technologies)
These data are the original observation records of planetary motion.
Through the processing and interpretation of the data, the information obtained includes:
Planetary trajectory diagram
Periodicity of planetary motion
Changes in the relative positions of planets with the sun and other celestial bodies
This information is the result of data processing and interpretation, which provides a preliminary understanding of planetary motion.
The process of knowledge generation is as follows:
Observe and learn:
Through long-term observation and recording, researchers can identify the trajectory and motion law of planets.
Hypothetical formation:
Based on the observation results, the hypothesis of planetary motion is put forward. For example, Kepler put forward the hypothesis that "the orbits of planets are elliptical".
Semantic integrity abstraction:
The observation results are given "complete" semantics through assumptions, forming a systematic understanding. For example, assume that "the orbits of all planets are elliptical" and confirm this assumption through further observation and calculation.
Semantic connection construction:
Link new knowledge with existing astronomical knowledge to form a systematic knowledge structure. For example, Kepler's law and Newton's law of universal gravitation are combined to form a complete understanding of planetary motion.
Knowledge verification and correction:
Through continuous observation and calculation, the hypothesis of planetary motion is verified, and it is revised and improved according to the new observation results. For example, by observing other planets, the universality of Kepler's law is confirmed.
8.4 Structured representation of knowledge
In this case, knowledge can be expressed as a semantic network, in which nodes represent astronomical concepts and edges represent semantic relationships between concepts. For example:
Node n:
Planetary orbit (ellipse)
orbital period
universal gravitation
Edge e:
Relationship between planetary orbit and orbital period
Relationship between orbital period and universal gravitation
This structural representation helps us to understand the complex system and abstract concepts of planetary motion.
9 The philosophical significance of knowledge
In the DIKWP model, knowledge is not only a record of observations and facts, but also a systematic understanding formed through assumptions and higher-order cognitive activities. The semantic integrity and systematicness of knowledge reflect the cognitive subject's profound understanding and interpretation of the world. Through hypothesis and abstraction, researchers can reveal the deep connection and internal logic between phenomena and provide a more comprehensive and profound understanding of the world.
The process of knowledge generation is not only the integration of existing data and information, but also the assumption and abstraction, which endows the observation results with complete semantics, thus forming systematic understanding and rules. This knowledge structure can not only explain phenomena, but also predict future behaviors and characteristics, providing a deeper understanding and guidance to the world.
10 Dynamic verification and correction of knowledge
The process of knowledge generation is a dynamic process, which involves how cognitive subjects relate different DIKWP content semantics through assumptions and high-level cognitive activities to form new knowledge semantics. In the cognitive space, this process includes not only the re-semantic combination and transformation of known DIKWP content, but also the generation of new cognitive understanding and knowledge semantics through this re-combination and transformation.
This dynamic nature is reflected in the process of knowledge generation and updating. Through continuous observation, learning and verification, cognitive subjects can form and improve a systematic knowledge structure. This knowledge structure can not only explain phenomena, but also predict future behaviors and characteristics, providing a deeper understanding and guidance to the world.
11 Conclusion of comprehensive technical report
Through the above detailed analysis, we can summarize the definition and generation process of data, information and knowledge in Professor Yucong Duan's DIKWP model. Professor Duan's definition emphasizes the following key points:
Semantic integrity: the semantics of knowledge concepts correspond to one or more "complete" semantics in cognitive space, and semantic integrity is abstracted through assumptions and higher-order cognitive activities.
Hypothesis and abstraction: knowledge is formed by the cognitive subject's semantic integrity abstraction of DIKWP content with the help of some assumptions.
Semantic network: the semantic structure of knowledge is a semantic network, in which nodes represent concepts and edges represent semantic relationships between concepts.
Systematic understanding: knowledge is a bridge of cognitive state transformation from non-understanding to understanding of DIKWP content, which emphasizes the understanding of the essence and internal relations of things.
Dynamic verification and correction: strengthen the confirmation of knowledge through verification and correction.
Sharing and dissemination: knowledge exists not only at the individual level, but also at the collective or social level, and is shared and disseminated through culture, education and inheritance.
Compared with other knowledge definitions and models, Professor Yucong Duan's definition pays more attention to the dynamic generation and semantic integrity of knowledge, and emphasizes that knowledge is abstracted and verified through assumptions and higher-order cognitive activities in the cognitive process. This definition
It is more suitable for explaining and understanding complex systems and abstract concepts, and also emphasizes the sharing and dissemination of knowledge at the social level.
12 Analysis of knowledge definition: Professor Yucong Duan's definition and other knowledge definitions and models
12.1 DIKW model (data-information-knowledge-wisdom)
Data: Original and unprocessed facts and observation records.
Information: data that has been processed and understood and given a specific meaning.
Knowledge: Further explanation and understanding of information, forming patterns and rules.
Wisdom: Use knowledge to make effective decisions and actions.
Hierarchical vs dynamic: DIKW model emphasizes the hierarchical and static storage of knowledge, and handles data, information, knowledge and wisdom in different levels. Professor Duan's definition emphasizes the dynamic generation and semantic integrity of knowledge, and constantly generates and verifies knowledge through high-level cognitive activities.
Semantic integrity: In Professor Duan's definition, the semantic integrity of knowledge is the core, while DIKW model pays more attention to the transformation process from information to knowledge, and lacks in-depth discussion on semantic integrity.
Cognitive process: Professor Duan's definition emphasizes that cognitive subjects form knowledge through hypothesis and abstraction, while DIKW model emphasizes information processing and understanding, and less involves the deep-seated mechanism of cognitive process.
12.2 SECI model (socialization, externalization, combination, internalization)
Explicit knowledge: it can be transmitted by writing and coding.
Tacit knowledge: personal experience and insight, which are difficult to transmit directly.
Four processes: socialization (recessive to recessive), externalization (recessive to dominant), combination (dominant to dominant) and internalization (dominant to recessive).
Dynamic vs structured: SECI model emphasizes the dynamic transformation and sharing process of knowledge within the organization, and the interaction between explicit and implicit knowledge. Professor Duan's definition emphasizes the semantic network and semantic integrity of knowledge and pays more attention to the structured representation of knowledge.
Explicit and tacit knowledge: SECI model focuses on the mutual transformation of explicit and tacit knowledge. Professor Duan's definition mainly focuses on the abstraction and verification of knowledge, and does not directly distinguish explicit and tacit knowledge, but also covers their interaction.
Application scenario: SECI model is suitable for describing the knowledge flow and sharing within an organization, while Professor Duan's definition is more suitable for theoretical construction and semantic understanding of knowledge.
12.3 Polanyi's tacit knowledge theory
Explicit knowledge: it can be formalized and transmitted.
Tacit knowledge: personal experience and skills that are difficult to formalize and transmit.
Individuality vs systematicness: Polanyi's theory emphasizes the importance and difficulty of transferring tacit knowledge, and pays attention to the individuality and experience of knowledge. Professor Duan's definition focuses on the semantic integrity and abstract process of knowledge, emphasizing the systematization and structure of knowledge.
Transitivity: Polanyi's theory holds that tacit knowledge is difficult to be transmitted by formal means, while Professor Duan's definition provides a structured representation of knowledge, emphasizing the sharing and dissemination of knowledge through high-level cognitive activities.
Cognitive process: Professor Duan's definition describes the cognitive process of knowledge generation in detail, including hypothesis, abstraction and verification, while Polanyi's theory mainly focuses on individual experience and skills of knowledge.
Five domains: simple, complex, complex, chaotic and disorderly, and the application and decision-making methods of knowledge in each domain are different.
Context vs abstraction: The Cynefin framework emphasizes the application and decision-making of knowledge in different situations, and is suitable for the decision-making of complex systems and problems. Professor Duan's definition emphasizes the abstraction and verification of knowledge, which is suitable for theoretical construction and semantic understanding of knowledge.
Application scenario: The Cynefin framework is suitable for real-time decision-making and situational analysis, while Professor Duan's definition is more suitable for long-term accumulation and systematic understanding of knowledge.
Knowledge representation: The Cynefin framework classifies knowledge application modes through five domains. Professor Duan's definition provides a semantic network representation of knowledge, emphasizing the structural relationship between concepts and semantics.
Through the above detailed analysis and comparison, we can sum up the differences and advantages between Professor Yucong Duan's DIKWP model and other models in the definition of data, information and knowledge.
Emphasize the semantic integrity of knowledge, and form systematic understanding and rules through high-level cognitive activities. The generation of knowledge is not a simple recording and processing, but a deep understanding and abstraction of data and information.
12.5.2 Hypothesis and Abstraction
The process of knowledge generation includes hypothesis, abstraction and verification, emphasizing the dynamic generation and continuous revision of knowledge. This is in contrast with the static hierarchy of DIKW model, and it is more suitable to describe the actual generation and application process of knowledge.
The structured representation of knowledge is a semantic network, with nodes representing concepts and edges representing semantic relationships between concepts. This representation helps to understand complex systems and abstract concepts, and provides a systematic structure of knowledge.
12.5.4 Systematic understanding
Knowledge is the bridge of the cognitive state transformation from never understanding to understanding of DIKWP content, which emphasizes the profound understanding of the essence and internal relations of things. This systematic understanding can explain and predict the behavior and characteristics of phenomena.
12.5.5 Dynamic verification and correction
The process of knowledge generation and verification is dynamic. Through constant observation, experiment and verification, the confirmation and correction of knowledge are strengthened. This dynamic nature enables knowledge to be constantly improved and updated, maintaining its applicability and accuracy.
12.5.6 Sharing and dissemination.
Knowledge exists not only at the individual level, but also at the collective or social level, and is shared and disseminated through culture, education and inheritance. This sharing and dissemination mechanism ensures that knowledge can be widely used and transmitted, and promotes the development of collective wisdom.
13 Comparison with other models
DIKW model
It emphasizes the hierarchical and static storage of knowledge and is suitable for knowledge management and classification.
Professor Duan's definition pays more attention to the dynamic generation and semantic integrity of knowledge, and is suitable for describing the actual generation and application process of knowledge.
SECI model
Emphasis on the dynamic transformation and sharing process of explicit and tacit knowledge is applicable to the knowledge flow within the organization.
Professor Duan's definition provides a structured representation of knowledge, which is more suitable for describing the semantic network and abstract process of knowledge.
Polanyi's tacit knowledge theory
Emphasize the importance and difficulty of tacit knowledge, pay attention to the individuality and experience of knowledge.
Professor Duan's definition provides a systematic and structured representation of knowledge, emphasizing the sharing and dissemination of knowledge through high-level cognitive activities.
Cynefin framework
Emphasizing the application and decision-making of knowledge in different situations is suitable for real-time decision-making of complex systems and problems.
Professor Duan's definition provides a semantic network representation of knowledge, which is suitable for theoretical construction and semantic understanding of knowledge.
Professor Yucong Duan's DIKWP model provides a higher-order and more abstract framework in the process of defining and generating knowledge, which is suitable for explaining and understanding complex systems and abstract concepts. Compared with other models, Professor Duan's definition pays more attention to the dynamic generation and semantic integrity of knowledge, and emphasizes the deep understanding and abstraction of knowledge in the cognitive process. Through in-depth analysis and comparison of data, information and knowledge, we can see the unique advantages and applicability of DIKWP model in knowledge management and application.
13.1 Comparative analysis of Professor Yucong Duan's knowledge definition and other knowledge definitions and models
characteristic | Professor Yucong Duan's Definition of Knowledge | DIKW model | SECI model | Polanyi's tacit knowledge theory | Cynefin framework |
definition | The semantics of knowledge concepts correspond to one or more "complete" semantics in cognitive space, and the semantic integrity is abstracted through assumptions and higher-order cognitive activities. | Knowledge is processed and understood information, which can be used for decision-making and action. | Knowledge is divided into explicit knowledge and tacit knowledge, which are transformed through four processes: socialization, externalization, combination and internalization. | Knowledge is divided into explicit knowledge and tacit knowledge, and tacit knowledge is personal experience and skills that are difficult to formalize and transmit. | The application of knowledge in different situations is different, and it is divided into five domains: simple, complex, complex, chaotic and disorderly. |
Key features | Semantic integrity, hypothesis and abstraction, semantic network, systematic understanding, dynamic verification and correction, sharing and dissemination. | Hierarchical and static, emphasizing the storage and management of knowledge. | Dynamic and dual (mutual transformation between explicit knowledge and tacit knowledge). | It is difficult to formalize and individualize, emphasizing personal experience and skills. | Situational and diverse, emphasizing that the application of knowledge and decision-making change according to different situations. |
Semantic integrity | Emphasis is placed on forming complete semantics and constructing systematic understanding through assumptions and abstract activities. | Do not emphasize, mainly pay attention to the transformation process from information to knowledge. | Part emphasizes the formation of systematic understanding through the transformation of explicit and tacit knowledge. | Do not emphasize, mainly pay attention to the individuality and difficult transmission of tacit knowledge. | Do not emphasize, mainly focus on the application of knowledge in different situations. |
Knowledge generation process | Observation and learning, hypothesis formation, semantic integrity abstraction, semantic connection construction, knowledge verification and correction. | Information processing and understanding, pattern recognition and formation rules. | Socialization, externalization, combination and internalization. | Tacit knowledge is formed through personal experience and skills, and it is difficult to transmit it directly. | Knowledge generation makes decisions according to different situations, and its application methods are different. |
knowledge representation | In semantic network, nodes represent concepts and edges represent semantic relationships between concepts. | Hierarchical structure, from data to information to knowledge and wisdom. | Dynamic transformation process, mutual transformation of explicit knowledge and tacit knowledge. | Personal experience and skills that are difficult to formalize are not easily transmitted through documents. | There are five domains, and the application and decision-making methods of knowledge in each domain are different. |
Static vs. dynamic | Dynamic, emphasizing the generation, verification and correction of knowledge. | Static, mainly focusing on the level and storage of knowledge. | Dynamic, emphasizing the process of knowledge transformation and sharing. | Static, mainly focusing on the individuality and difficulty of transferring tacit knowledge. | Dynamic, knowledge application changes according to the situation. |
Individuality vs. sociality | Emphasis is placed on the sharing and dissemination of knowledge at the individual and social levels through culture, education and inheritance. | Focus on the personal level of knowledge, not on the social level. | Emphasize the sociality of knowledge, through the transformation and sharing process within the organization. | Emphasize the individuality of knowledge, focusing on personal experience and skills. | Emphasizing the application of knowledge in different situations is suitable for the decision-making of complex systems and problems. |
Abstraction and generalization | Emphasize that systematic knowledge is formed through higher-order cognitive activities and assumptions. | Do not emphasize, mainly focus on the processing and understanding of information. | Part emphasizes the formation of systematic understanding through the transformation of explicit and tacit knowledge. | Do not emphasize, mainly pay attention to the individuality and difficult transmission of tacit knowledge. | Do not emphasize, mainly focus on the application of knowledge and decision-making methods. |
Verification and correction | It is emphasized that the correctness and validity of the hypothesis are verified by further observation and experiment, and it is revised and improved according to the new information. | Do not emphasize, mainly focus on the storage and management of knowledge. | Emphasis is placed on verifying and perfecting knowledge through continuous knowledge transformation and sharing process. | Do not emphasize, mainly pay attention to the individuality and difficult transmission of tacit knowledge. | Emphasis is placed on the application and decision-making of knowledge according to different situations to verify its effectiveness. |
Philosophical significance | Knowledge is the profound understanding and interpretation of the world by cognitive subjects, and it forms systematic knowledge through abstraction and generalization. | Knowledge is the further processing and understanding of information, mainly focusing on its application and decision-making function. | Knowledge is the mutual transformation of explicit and tacit knowledge, which is formed through sharing and dissemination within the organization. | Knowledge is the embodiment of personal experience and skills, and it is difficult to transfer it through formal means. | Knowledge is a means of decision-making and application according to the situation, emphasizing its diversity and situational adaptability. |
Through comparative analysis, we can see that Professor Yucong Duan's knowledge definition emphasizes the semantic integrity, abstract process, dynamic verification and semantic network structure of knowledge on the basis of the existing knowledge model. Compared with other models, Professor Duan's definition pays more attention to the process of knowledge generation and verification, and emphasizes the understanding of the essence and internal relations of things. This definition is more suitable for explaining and understanding complex systems and abstract concepts, and also emphasizes the sharing and dissemination of knowledge at the social level.
Professor Yucong Duan's DIKWP model provides a high-order, dynamic and structured framework for the generation and application of knowledge, so that it can better adapt to the understanding and interpretation of complex systems and abstract concepts. Through in-depth analysis and comparison of data, information and knowledge, we can see the unique advantages and applicability of DIKWP model in knowledge management and application. This provides a new perspective and method for knowledge management, education and research, and promotes knowledge sharing, dissemination and application.
13.2 Comparative Analysis of Professor Yucong Duan's Data Definition and Other Data Definitions and Models
characteristic | Professor Yucong Duan's Data Definition | DIKW model | SECI model | Polanyi's tacit knowledge theory | Cynefin framework |
definition | The original facts or observation records confirmed by the cognitive subject, and the cognitive object confirmed by the conceptual space. | Original and unprocessed facts and observation records. | Original facts and records. | Tacit knowledge does not explicitly involve data. | Data has different applications in different domains. |
semantics | Semantic matching and confirmation are carried out through conceptual space or semantic space. | The original record before giving a specific meaning. | As part of explicit knowledge. | Tacit knowledge focuses on personal experience and does not involve data. | Does not explicitly involve semantic processing of data. |
Static vs. dynamic | Static, through the confirmation of cognitive subject. | Static, as the basis of knowledge generation. | Static as a part of explicit knowledge. | Static, focusing on personal experience and skills. | Static, processing according to different domains. |
Treatment process | Classification and organization through cognitive subjects. | Convert into information through processing and understanding. | Through the process of socialization. | Unclear treatment process. | The data is applied according to the situation. |
Individuality vs. sociality | Data processing is mainly embodied in the process of individual cognition. | Individual processing, emphasizing data accumulation. | Handling and sharing in the process of socialization. | Mainly concerned with individual experience, not involving sociality. | Individual or collective treatment according to the situation. |
13.3 Comparative Analysis of Professor Yucong Duan's Information Definition and Other Information Definitions and Models
characteristic | Definition of Professor Yucong Duan | DIKW model | SECI model | Polanyi's tacit knowledge theory | Cynefin framework |
definition | Through the purpose of cognitive subject, the data is semantically associated with existing cognitive objects to identify differences. | The processed and understood data is endowed with specific meaning. | Part of transforming tacit knowledge into explicit knowledge. | Tacit knowledge is difficult to be clearly transformed into information. | Information has different applications in different domains. |
semantics | New information semantics are formed through semantic matching and association. | The result of data processing and understanding. | The externalization result of explicit knowledge. | Mainly concerned with tacit knowledge, limited information transmission. | Semantic processing of ambiguous information. |
Static vs. dynamic | Dynamic, generated and verified by the purpose of cognitive subject. | Static, as the basis of knowledge generation. | Dynamic, emphasizing the interaction between explicit and tacit knowledge. | Mainly concerned with tacit knowledge, less information processing. | Dynamic, processing and application according to the situation. |
Treatment process | Through the purpose and cognitive process of cognitive subject, a new semantic association is formed. | By processing and understanding data, information is formed. | Through the process of externalization, tacit knowledge is transformed into explicit knowledge. | Mainly through personal experience and skills transfer. | Information is processed and applied according to the situation. |
Individuality vs. sociality | Information processing involves both individuals and social sharing and communication. | Give priority to individual processing, emphasizing information accumulation. | Handling and sharing in the process of socialization. | Mainly concerned with individual experience, limited information transmission. | Individual or collective treatment according to the situation. |
13.4 Overall Comparative Analysis of Professor Yucong Duan's Data-Information-Knowledge
characteristic | Definition of Professor Yucong Duan | DIKW model | SECI model | Polanyi's tacit knowledge theory | Cynefin framework |
Static vs. dynamic | Emphasize the dynamic generation and verification process, and the generation and application of knowledge is continuous. | Static storage of data, information and knowledge, emphasizing hierarchy. | Emphasize the dynamic transformation and sharing process of knowledge. | The individuality and experience of tacit knowledge are difficult to formalize. | Emphasize the dynamic application of knowledge and situational adaptability. |
Individuality vs. sociality | Emphasize the individual and social sharing of knowledge and spread it through culture and education. | Data and information are mainly handled by individuals, and knowledge is applied to individual decision-making. | Emphasize the transformation and sharing of knowledge within the organization. | Knowledge is mainly personal experience and skills, and it is difficult to socialize. | Knowledge is applied individually or collectively according to the situation. |
Abstraction and generalization | Emphasize the formation of systematic knowledge through higher-order cognitive activities and assumptions. | Do not emphasize, mainly focus on the processing and understanding of information. | Part emphasizes the formation of systematic understanding through the transformation of explicit and tacit knowledge. | Do not emphasize, mainly pay attention to the individuality and difficult transmission of tacit knowledge. | Do not emphasize, mainly focus on the application of knowledge and decision-making methods. |
Verification and correction | It is emphasized that the correctness and validity of the hypothesis are verified by observation and experiment, and it is revised and improved according to the new information. | Do not emphasize, mainly focus on the storage and management of knowledge. | Emphasis is placed on verifying and perfecting knowledge through the continuous process of knowledge transformation and sharing. | Do not emphasize, mainly pay attention to the individuality and difficult transmission of tacit knowledge. | It emphasizes the application and decision-making of knowledge according to different situations to verify its effectiveness. |
Philosophical significance | Knowledge is the profound understanding and interpretation of the world by cognitive subjects, and it forms systematic knowledge through abstraction and generalization. | Knowledge is the further processing and understanding of information, mainly focusing on its application and decision-making function. | Knowledge is the mutual transformation of explicit and tacit knowledge, which is formed through sharing and dissemination within the organization. | Knowledge is the embodiment of personal experience and skills, and it is difficult to transfer it through formal means. | Knowledge is a means of decision-making and application according to the situation, emphasizing its diversity and situational adaptability. |
14 Comparative analysis of data, information and knowledge in different spaces
14.1 Professor Yucong Duan's DIKWP model: the definition of three spaces
Definition: Conceptual space is a space for cognitive subjects to communicate and recognize through natural language, symbols and other forms. In this space, data, information and knowledge exist as concrete concepts and are expressed through semantic networks and concept maps.
Function: Help cognitive subjects to classify and organize specific facts and observations, and form systematic understanding and rules.
14.1.2 Semantic Space (semantic space)
Definition: Semantic space is the space where cognitive subjects understand and deal with the internal semantic relations of concepts. In this space, cognitive subjects understand and generate new knowledge through semantic matching, association and transformation.
Function: to form new semantic relations through semantic matching and association, so as to generate and verify knowledge.
14.1.3 Cognitive Space (cognitive space)
Definition: Cognitive space is the internal psychological space for cognitive subjects to think, learn and understand. In this space, cognitive subjects form a profound understanding and explanation of the world through cognitive activities such as observation, hypothesis, abstraction and verification.
Function: Transform data and information into systematic knowledge through high-level cognitive activities, and constantly improve and update knowledge in the process of dynamic verification and correction.
14.2 Comparative Analysis Table of Data, Information and Knowledge in Different Spaces
14.2.1 Comparative Analysis of Data
characteristic | Professor Yucong Duan's Definition (Conceptual Space) | Professor Yucong Duan's Definition (Semantic Space) | Professor Yucong Duan's Definition (Cognitive Space) |
definition | The original facts or observation records confirmed by the cognitive subject, and the cognitive object confirmed by the conceptual space. | The data confirmed by semantic matching and association have specific semantic attributes. | Through the thinking and classification of cognitive subjects, concrete observation records and facts are formed. |
function | Classify and organize specific facts and observations. | Form the semantic connection of data to ensure the consistency of data in the cognitive process. | Through thinking and classification, a preliminary cognitive object is formed. |
Static vs. dynamic | Static, through the confirmation of cognitive subject. | Dynamic, processed through semantic matching and association. | Dynamic, processing and classification through cognitive activities. |
Treatment process | Classification and organization through cognitive subjects. | Through semantic matching and association in semantic space. | Through the thinking and classification of cognitive space. |
14.2.2 Comparative Analysis of Information
characteristic | Professor Yucong Duan's Definition (Conceptual Space) | Professor Yucong Duan's Definition (Semantic Space) | Professor Yucong Duan's Definition (Cognitive Space) |
definition | Through the purpose of cognitive subject, the data is semantically associated with existing cognitive objects to identify differences. | New information semantics are formed through semantic matching and association, and differences of data are identified. | Through the purpose and cognitive process of the cognitive subject, new semantic associations and information are formed. |
function | Help cognitive subjects identify differences and connections between data. | Form new semantic connections to ensure the consistency of information in the cognitive process. | Through cognitive process, new cognitive understanding and information are formed. |
Static vs. dynamic | Dynamic, generated and verified by the purpose of cognitive subject. | Dynamic, processed through semantic matching and association. | Dynamic, processing and classification through cognitive activities. |
Treatment process | Through the purpose and cognitive process of cognitive subject, a new semantic association is formed. | Through semantic matching and association of semantic space, new information semantics are formed. | Through the thinking and purpose of cognitive space, new cognition and information are formed. |
14.2.3 Comparative analysis of Knowledge
characteristic | Professor Yucong Duan's Definition (Conceptual Space) | Professor Yucong Duan's Definition (Semantic Space) | Professor Yucong Duan's Definition (Cognitive Space) |
definition | The semantics of knowledge concept corresponds to one or more "complete" semantics in cognitive space. | Form systematic understanding and rules through semantic matching and association. | Form systematic knowledge and understanding through high-level cognitive activities and assumptions. |
function | Form systematic concepts and rules to help cognitive subjects understand and explain the world. | Form a systematic semantic connection to ensure the consistency of knowledge in the cognitive process. | Through cognitive activities, a profound understanding and explanation of the world is formed. |
Static vs. dynamic | Dynamic, knowledge generation and verification is a continuous process. | Dynamic, verified and corrected through semantic matching and association. | Dynamic, verified and corrected through cognitive activities. |
Treatment process | Through observation, hypothesis, abstraction, verification and correction, systematic knowledge is formed. | Knowledge is formed and verified through semantic matching and association in semantic space. | Knowledge is formed and verified through higher-order cognitive activities in cognitive space. |
14.2.4 Overall Comparative Analysis of Data-Information-Knowledge
characteristic | Professor Yucong Duan's Definition (Conceptual Space) | Professor Yucong Duan's Definition (Semantic Space) | Professor Yucong Duan's Definition (Cognitive Space) |
data | The original facts or observation records confirmed by the cognitive subject, and the cognitive object confirmed by the conceptual space. | The data confirmed by semantic matching and association have specific semantic attributes. | Through the thinking and classification of cognitive subjects, concrete observation records and facts are formed. |
information | Through the purpose of cognitive subject, the data is semantically associated with existing cognitive objects to identify differences. | New information semantics are formed through semantic matching and association, and differences of data are identified. | Through the purpose and cognitive process of the cognitive subject, new semantic associations and information are formed. |
knowledge | The semantics of knowledge concept corresponds to one or more "complete" semantics in cognitive space. | Form systematic understanding and rules through semantic matching and association. | Form systematic knowledge and understanding through high-level cognitive activities and assumptions. |
Semantic integrity | Form a systematic understanding through high-level cognitive activities. | Semantic integrity is formed through semantic matching and association. | Semantic integrity is formed through cognitive activities and assumptions. |
Treatment process | Data are classified and organized by cognitive subjects; Information is formed through purpose and cognitive process; Knowledge is formed by hypothesis, abstraction and verification. | Data is confirmed by semantic matching and association; Information is formed through semantic association; Knowledge is formed through semantic verification and revision. | Data are classified through cognitive activities; Information is formed through cognitive purpose; Knowledge is formed and verified through high-level cognitive activities. |
Static vs. dynamic | Emphasize the dynamic generation and verification process, and the generation and application of knowledge is continuous. | Emphasis is placed on the dynamic nature of semantic matching and association, and the generation and verification of knowledge is continuous. | Emphasize the dynamic nature of cognitive activities, and the generation and verification of knowledge is continuous. |
Individuality vs. sociality | Emphasize the individual and social sharing of knowledge and spread it through culture and education. | Emphasize the individuality and sociality of semantic connection, and spread it through semantic association and sharing. | Emphasize the individuality and sociality of cognitive activities and spread them through cognitive processes. |
Abstraction and generalization | Form systematic knowledge through higher-order cognitive activities and assumptions. | Form a systematic understanding through semantic matching and association. | Form a systematic understanding through cognitive activities and assumptions. |
Verification and correction | The correctness and validity of the hypothesis are verified by observation and experiment, and it is revised and improved according to the new information. | Correct knowledge through semantic verification and association, and improve it according to new information. | Verify and correct knowledge through cognitive activities and improve it according to new information. |
Philosophical significance | Knowledge is the profound understanding and interpretation of the world by cognitive subjects, and it forms systematic knowledge through abstraction and generalization. | Knowledge is a systematic understanding formed through semantic connection and matching, which reflects the cognitive subject's semantic cognition of the world. | Knowledge is formed through high-order cognitive activities and assumptions, and it is a profound understanding and explanation of the world. |
14.3 Comparison of data, information and knowledge with other models in different spaces
14.3.1 Comparative Analysis of Conceptual Space
characteristic | Definition of Professor Yucong Duan | DIKW model | SECI model | Polanyi's tacit knowledge theory | Cynefin framework |
data definition | The original facts or observation records confirmed by the cognitive subject, and the cognitive object confirmed by the conceptual space. | Original and unprocessed facts and observation records. | Original facts and records. | Tacit knowledge does not explicitly involve data. | Data has different applications in different domains. |
Information definition | Through the purpose of cognitive subject, the data is semantically associated with existing cognitive objects to identify differences. | The processed and understood data is endowed with specific meaning. | Part of transforming tacit knowledge into explicit knowledge. | Tacit knowledge is difficult to be clearly transformed into information. | Information has different applications in different domains. |
Knowledge definition | The semantics of knowledge concept corresponds to one or more "complete" semantics in cognitive space, which is formed through assumptions and higher-order cognitive activities. | The processed and understood information can be used for decision-making and action. | Knowledge is divided into explicit knowledge and tacit knowledge, which are generated through transformation. | Tacit knowledge is personal experience and skill, which is difficult to formalize. | Knowledge is applied according to different situations. |
Static vs. dynamic | Emphasize the dynamic generation and verification process, and the generation and application of knowledge is continuous. | Static storage of data, information and knowledge, emphasizing hierarchy. | Emphasize the dynamic transformation and sharing process of knowledge. | The individuality and experience of tacit knowledge are difficult to formalize. | Emphasize the dynamic application of knowledge and situational adaptability. |
Individuality vs. sociality | Emphasize the individual and social sharing of knowledge and spread it through culture and education. | Data and information are mainly handled by individuals, and knowledge is applied to individual decision-making. | Emphasize the transformation and sharing of knowledge within the organization. | Knowledge is mainly personal experience and skills, and it is difficult to socialize. | Knowledge is applied individually or collectively according to the situation. |
Abstraction and generalization | Form systematic knowledge through higher-order cognitive activities and assumptions. | Do not emphasize, mainly focus on the processing and understanding of information. | Part emphasizes the formation of systematic understanding through the transformation of explicit and tacit knowledge. | Do not emphasize, mainly pay attention to the individuality and difficult transmission of tacit knowledge. | Do not emphasize, mainly focus on the application of knowledge and decision-making methods. |
Verification and correction | The correctness and validity of the hypothesis are verified by observation and experiment, and it is revised and improved according to the new information. | Do not emphasize, mainly focus on the storage and management of knowledge. | Emphasis is placed on verifying and perfecting knowledge through the continuous process of knowledge transformation and sharing. | Do not emphasize, mainly pay attention to the individuality and difficult transmission of tacit knowledge. | It emphasizes the application and decision-making of knowledge according to different situations to verify its effectiveness. |
Philosophical significance | Knowledge is the profound understanding and interpretation of the world by cognitive subjects, and it forms systematic knowledge through abstraction and generalization. | Knowledge is the further processing and understanding of information, mainly focusing on its application and decision-making function. | Knowledge is the mutual transformation of explicit and tacit knowledge, which is formed through sharing and dissemination within the organization. | Knowledge is the embodiment of personal experience and skills, and it is difficult to transfer it through formal means. | Knowledge is a means of decision-making and application according to the situation, emphasizing its diversity and situational adaptability. |
14.3.2 Comparative Analysis of Semantic Space
characteristic | Definition of Professor Yucong Duan | DIKW model | SECI model | Polanyi's tacit knowledge theory | Cynefin framework |
data definition | The data confirmed by semantic matching and association have specific semantic attributes. | The original data is semantically labeled in the semantic space. | Data is semantically labeled by explicit knowledge in the process of transformation. | Tacit knowledge is mainly embodied by implicit semantics in personal experience. | Data are semantically labeled and processed according to different situations. |
Information definition | New information semantics are formed through semantic matching and association, and differences of data are identified. | After data processing, it is given a specific meaning to form the semantics of information. | Information is semantically labeled in the externalization of explicit knowledge. | Tacit knowledge is expressed as information through implied semantics and context. | Information is semantically labeled and processed according to different situations. |
Knowledge definition | Form systematic understanding and rules through semantic matching and association. | Information forms the semantics of knowledge through semantic association. | Knowledge forms semantic association in the process of transformation between explicit and implicit knowledge. | Tacit knowledge forms deep understanding mainly through implied semantics. | Knowledge is semantically associated and processed according to different situations. |
Static vs. dynamic | Dynamic, processed and verified through semantic matching and association. | The semantic association of knowledge is relatively static, mainly focusing on hierarchy. | Dynamic, semantic processing through the interaction of explicit and tacit knowledge. | The implicit semantics of knowledge is dynamic and difficult to formalize. | Emphasize the dynamic processing and situational adaptability of knowledge semantics. |
Individuality vs. sociality | Emphasize the individuality and sociality of semantic connection, and spread it through semantic association and sharing. | The semantic relevance of knowledge is mainly at the individual level, and its sociality is weak. | Emphasis is placed on semantic transformation and sharing of knowledge and sociality. | The implied semantics of knowledge is mainly at the individual level, which is difficult to socialize. | Knowledge semantics is related and processed individually or collectively according to the situation. |
Abstraction and generalization | Form a systematic understanding through semantic matching and association. | Abstraction and generalization through hierarchical semantic association. | Abstraction and generalization through the transformation of explicit and tacit knowledge. | Abstraction and generalization through deep understanding of implied semantics. | Semantic abstraction and generalization according to the situation. |
Verification and correction | Correct knowledge through semantic verification and association, and improve it according to new information. | The semantic verification and correction of knowledge is weak, mainly relying on static level. | Emphasize the semantic transformation and verification process of knowledge, and improve it through interaction. | Semantic verification of tacit knowledge is difficult to formalize and depends on personal experience. | Semantic verification and correction of knowledge are carried out according to the situation. |
Philosophical significance | Knowledge is a systematic understanding formed through semantic connection and matching, which reflects the cognitive subject's semantic cognition of the world. | The semantics of knowledge is mainly formed after information processing, and its application and decision-making function are concerned. | Knowledge is a semantic connection formed through the transformation and sharing of explicit and implicit knowledge. | The implied semantics of knowledge is mainly embodied through personal experience, and it is difficult to formally transmit it. | The semantics of knowledge are related according to the situation, emphasizing its diversity and situational adaptability. |
14.3.3 Comparative Analysis of Cognitive Space
characteristic | Definition of Professor Yucong Duan | DIKW model | SECI model | Polanyi's tacit knowledge theory | Cynefin framework |
data definition | Through the thinking and classification of cognitive subjects, concrete observation records and facts are formed. | Raw data are classified and processed through cognitive activities. | Data is transformed into explicit knowledge in the cognitive process. | Tacit knowledge is mainly embodied through personal experience and implicit cognitive activities. | The data is cognitively processed according to different situations. |
Information definition | Through the purpose and cognitive process of the cognitive subject, new semantic associations and information are formed. | Information is processed and understood through cognitive activities. | Information is externalized into explicit knowledge in the cognitive process. | Information is transmitted through tacit knowledge and personal experience. | Information is cognitively processed according to different situations. |
Knowledge definition | Form systematic knowledge and understanding through high-level cognitive activities and assumptions. | Information is processed and understood to form knowledge for decision-making and action. | Knowledge is generated through cognitive activities and the transformation of explicit and implicit knowledge. | Knowledge is deeply understood mainly through tacit knowledge and personal experience. | Knowledge is cognitively processed and applied according to different situations. |
Static vs. dynamic | Dynamic, verified and corrected through cognitive activities. | Static storage and hierarchical management of knowledge. | Dynamic, cognitive processing through the interaction of explicit and tacit knowledge. | The implicit cognition of knowledge is dynamic and difficult to formalize. | Emphasize the dynamic cognition and situational adaptability of knowledge. |
Individuality vs. sociality | Emphasize the individuality and sociality of cognitive activities and spread them through cognitive processes. | Knowledge is mainly handled at the individual level, but less at the social level. | Emphasize the cognitive transformation and sharing of knowledge in the organization, and pay attention to sociality. | Knowledge is mainly processed through individual experience and tacit cognition. | The cognition of knowledge is handled individually or collectively according to the situation. |
Abstraction and generalization | Form a systematic understanding through cognitive activities and assumptions. | Knowledge is formed by abstraction and generalization after information processing. | Abstract and summarize through the cognitive transformation of explicit and tacit knowledge. | Abstraction and generalization through tacit knowledge and personal experience. | Cognitive abstraction and generalization according to the situation. |
Verification and correction | Verify and correct knowledge through cognitive activities and improve it according to new information. | Knowledge verification and correction are less, mainly relying on static storage. | Emphasize the cognitive transformation and verification of knowledge, and correct it through interaction. | The verification of knowledge is difficult to formalize, mainly through personal experience. | Knowledge is verified and corrected according to the situation. |
Philosophical significance | Knowledge is formed through high-order cognitive activities and assumptions, and it is a profound understanding and explanation of the world. | Knowledge is the result of further processing and understanding of information, which is mainly used for decision-making. | Knowledge is formed through the cognitive interaction and transformation of explicit and implicit knowledge. | Knowledge is deeply understood mainly through implicit cognition and personal experience, which is difficult to formalize. | Knowledge is recognized according to the situation, emphasizing its diversity and situational adaptability. |
In the conceptual space, Professor Yucong Duan's definition emphasizes that data, information and knowledge are classified and organized through the confirmation of cognitive subjects, purpose and higher-order cognitive activities, forming systematic understanding and rules. Compared with other models, Professor Duan's definition pays more attention to the dynamic generation and verification process of knowledge and emphasizes the individual and social sharing of knowledge.
In the semantic space, Professor Yucong Duan's definition forms systematic understanding and rules of data, information and knowledge through semantic matching and association. In contrast, DIKW model focuses on hierarchical semantic association, SECI model emphasizes the transformation of explicit and tacit knowledge, Polanyi's tacit knowledge theory focuses on the implicit semantics of personal experience, and Cynefin framework deals with semantics according to situations.
In the cognitive space, Professor Yucong Duan's definition forms systematic knowledge and understanding through high-level cognitive activities and assumptions. Compared with other models, DIKW model pays attention to the static storage and hierarchical management of knowledge, SECI model emphasizes the interaction and transformation of explicit and tacit knowledge, Polanyi's tacit knowledge theory focuses on personal experience and tacit cognition, and Cynefin framework carries out dynamic cognitive processing according to the situation.
Professor Yucong Duan's DIKWP model provides a higher-order, dynamic and structured framework for the definition of data, information and knowledge by distinguishing conceptual space, semantic space and cognitive space in detail. Compared with other knowledge definitions and models, Professor Duan's definition pays more attention to the dynamic generation, verification and revision of knowledge, and emphasizes the individual and social sharing of knowledge, which is suitable for the understanding and interpretation of complex systems and abstract concepts.
Through comparative analysis, this comprehensive technical report shows the unique advantages and applicability of Professor Yucong Duan's DIKWP model in the definition of data, information and knowledge, provides a new perspective and method for knowledge management, education and research, and promotes the sharing, dissemination and application of knowledge.
Through comparative analysis, we discuss the definitions of data, information and knowledge in Professor Yucong Duan's DIKWP model in detail, and compare it with other main knowledge definitions and models. Professor Duan's definition emphasizes the dynamic generation of knowledge, semantic integrity, hypothesis and abstraction, and the process of verification and correction. This definition is not only suitable for theoretical construction and semantic understanding of knowledge, but also provides a new perspective and method for knowledge management and application.
When comparing other models, we found that:
DIKW model focuses on the hierarchical and static storage of knowledge, while Professor Duan's definition emphasizes the dynamic generation and verification of knowledge.
SECI model emphasizes the interaction and transformation of explicit and tacit knowledge, which is suitable for the knowledge flow within the organization, while Professor Duan's definition provides a structured representation of knowledge and is more suitable for describing the generation process of knowledge.
Polanyi's tacit knowledge theory pays attention to the individuality and difficult transmission of tacit knowledge, while Professor Duan's definition emphasizes the systematic and structured representation of knowledge.
The framework of Cynefin emphasizes the application and decision-making of knowledge in different situations, which is suitable for real-time decision-making of complex systems and problems. Professor Duan's definition provides a semantic network representation of knowledge, which is suitable for theoretical construction and semantic understanding of knowledge.
Generally speaking, Professor Yucong Duan's DIKWP model provides a high-order, dynamic and structured framework in the process of knowledge definition and generation, which is better adapted to the understanding and interpretation of complex systems and abstract concepts. Compared with other models, Professor Duan's definition not only pays attention to the process of knowledge generation and verification, but also emphasizes the sharing and dissemination of knowledge at the social level, which embodies the importance and role of knowledge in individual and collective cognition.
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