YucongDuan的个人博客分享 http://blog.sciencenet.cn/u/YucongDuan

博文

Definition of Knowledge (Conceptual and Semantic) - DIKWP

已有 321 次阅读 2024-5-22 09:10 |系统分类:论文交流

 

 

 

 

Prof. Yucong Duan Proposes Definition of Knowledge (Conceptual and Semantic) - DIKWP-Knowledge

 

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

(Emailduanyucong@hotmail.com)

 

 

 

 

Catalog

 

1 Yucong Duan's definition of knowledge (concept and semantics)

2 The concept and semantics of DIKWP-Knowledge Concept

2.1 DIKWP-Knowledge Concept

2.2 DIKWP-Knowledge Semantics

3 The generation process of DIKWP-Knowledge Semantics

4 Mathematical representation of DIKWP-Knowledge

5 Processing and generation of DIKWP-Knowledge Semantics

6 Cognition and construction of DIKWP-Knowledge

7 The philosophical significance of DIKWP-Knowledge

8 Dynamic nature of DIKWP-Knowledge Semantics

9 Case 1: Planetary Motion in Astronomy

9.1 Data

9.2 Information

9.3 Knowledge

9.4 Structured representation of knowledge

10 Case 2: Studying bird migration's behavior

10.1 Data

10.2 Information

10.3 Knowledge

10.4 Detailed explanation of specific case steps

10.5 Mathematical representation of knowledge

10.6 Philosophical significance of knowledge

11 Comparative analysis

11.1 Professor Yucong Duan's definition of knowledge

11.2 Compare other major knowledge definitions and models

11.2.1 DIKW model (data-information-knowledge-wisdom)

11.2.2 SECI Model of Nonaka and Takeuchi (Socialization, Externalization, Combination, Internalization)

11.2.3 Polanyi's tacit knowledge theory

11.2.4 Cynefin framework

11.3 Comprehensive analysis

Conclusion

References

 

Prof Yucong Duan Proposes Definition of Knowledge (Conceptual and Semantic) - DIKWP-Knowledge

1 Yucong Duan's definition of knowledge (concept and semantics)

The semantics of the concept of DIKWP-Knowledge corresponds to one or more "complete" semantics in the cognitive space. The semantics of knowledge concept is the understanding and explanation of the semantics between the contents of DIKWP (that is, the construction of the semantic connection between the contents of DIKWP and the existing contents of DIKWP, which forms the cognitive input of cognitive interaction, and can correspond to one or more "complete" semantics bearing cognitive complete purpose confirmation in a higher-order cognitive space.When dealing with knowledge concepts, the brain abstracts at least one concept or pattern corresponding to complete semantics through observation and learning. 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 by assuming (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" has complete semantics.

Knowledge K is a semantic network, in which nodes represent concepts and edges represent semantic relationships between concepts:

K=(N, E), where N represents a set of concepts and E represents a set of relationships between concepts.

This definition defines knowledge cognition as a higher cognitive achievement, emphasizing the structure of knowledge (such as semantic network) and the ability to capture complete semantics, which is very important for understanding complex systems and abstract concepts.

Knowledge is a bridge based on complete semantics, which transforms the content of DIKWP from not understanding the corresponding state to understanding the corresponding cognitive state, and strengthens the confirmation of knowledge through verification. The construction of knowledge depends not only on the accumulation of data and information, but also on the so-called 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 (corresponding to semantic space in conceptual 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.

 

2 The concept and semantics of DIKWP-Knowledge Concept

2.1 DIKWP-Knowledge Concept

In DIKWP model, the concept of knowledge corresponds to one or more "complete" semantics in cognitive space. The formation of the concept of knowledge is that the cognitive subject carries out semantic integrity abstraction activities on the contents of DIKWP through certain assumptions, and obtains the semantic understanding and explanation between the contents of DIKWP. This kind of understanding and explanation can correspond to one or more "complete" semantics that bear the complete confirmation of cognition in the cognitive space of cognitive subject.

The concept of knowledge is not only the simple accumulation of data and information, but also the abstraction of some observation results by cognitive subjects in high-level cognitive activities to form systematic understanding and rules. This rule or pattern can explain and predict the behavior or characteristics of cognitive objects, and provide a deeper understanding.

 

2.2 DIKWP-Knowledge Semantics

The semantics of knowledge means that the cognitive subject abstracts the semantic integrity of DIKWP content with the help of assumptions, thus forming an understanding and explanation of the semantic relationship between cognitive objects. Knowledge semantics emphasizes that cognitive subjects endow some observations with "complete" semantics through high-level cognitive activities, thus forming knowledge with integrity and regularity.

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.

 

3 The generation process of DIKWP-Knowledge Semantics

The generation process of DIKWP-Knowledge Semantics includes the following steps:

Observation and learning: cognitive subjects abstract patterns or concepts from specific data and information through observation and learning.

Hypothesis formation: through high-order cognitive activities, some observations are hypothesized and given "complete" semantics. For example, by observing some white swans, suppose that "all swans are white".

Semantic integrity abstraction: abstract the observation results through the complete semantics formed by hypothesis, and form a regular understanding of cognitive objects.

Semantic connection construction: through the cognitive activities of cognitive subjects, new knowledge semantics are connected with existing cognitive contents to form a systematic knowledge structure.

Knowledge verification and correction: verify the formed knowledge, test its effectiveness through actual observation and experiment, and correct and improve it according to new information.

 

4 Mathematical representation of DIKWP-Knowledge

In DIKWP model, the mathematical representation of DIKWP-Knowledge is helpful to understand its integrity and structure. The semantic attribute set S of knowledge concept is expressed as:

S={f1,f2,,fn}

Among them, fi represents a semantic feature of knowledge concept. The knowledge concept set K contains all instances that share a complete semantic attribute set:

K={kk sharing S}

The process of knowledge generation can be expressed as:

K:XY

Among them, X represents the set or combination of data semantics, information semantics, knowledge semantics, wisdom semantics and purpose semantics (that is, DIKWP content semantics), and Y represents the generated new knowledge semantic association.

 

5 Processing and generation of DIKWP-Knowledge Semantics

DIKWP-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.

DIKWP-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 DIKWP-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.

In the process of knowledge generation, the cognitive subject processes and generates knowledge semantics through the following steps:

Pattern recognition: Identify patterns in data and information through observation and learning.

Hypothesis construction: based on the identified pattern, put forward the hypothesis and give it complete semantics.

Semantic integration: integrating new assumptions with existing knowledge systems to form systematic knowledge.

Verification and revision: constantly revise and improve the knowledge system by verifying the effectiveness of new knowledge.

 

6 Cognition and construction of DIKWP-Knowledge

The generation and understanding of DIKWP-Knowledge is an active construction process, which depends on the existing DIKWP content and hypothesis-driven cognitive framework. The diversity and depth of knowledge semantics are reflected in its integrity and structure. Knowledge not only contains the semantic integrity of DIKWP content, but also creates new knowledge by linking these complete semantics with the existing knowledge structure. This dynamic cognitive structure updating process is the key to cognitive development and knowledge growth.

The process of DIKWP-Knowledge generation is not only the integration of existing data and information, but also the formation of systematic understanding and rules by giving complete semantics to observation results through assumptions and higher-order cognitive activities. This process includes the following aspects:

Hypothesis verification: verify the correctness and effectiveness of the hypothesis through experiments and observations.

DIKWP-Knowledge expansion: according to the new observation results and experimental data, expand and improve the existing knowledge system.

DIKWP-Knowledge transfer: through communication and education, knowledge is transferred to other cognitive subjects to form a shared knowledge system.

 

7 The philosophical significance of DIKWP-Knowledge

In the DIKWP model, DIKWP-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. The process of knowledge generation emphasizes the initiative and creativity of cognitive subjects in understanding and explaining the world. Through hypothesis and abstraction, some observations are given complete semantics, thus forming systematic knowledge.

DIKWP-Knowledge Semantics is not only the aggregation or reorganization of DIKWP content semantics, but also the creation of a new semantic association, which reflects the active exploration and interpretation of the world by cognitive subjects. Through hypothesis and higher-order cognitive activities, the process of knowledge generation can reveal the deep connection and internal logic between phenomena and provide a more comprehensive and profound understanding of the world.

 

8 Dynamic nature of DIKWP-Knowledge Semantics

The generation of DIKWP-Knowledge Semantics 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 DIKWP-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.

 

9 Case 1: 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.

9.1 Data

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.

9.2 Information

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.

9.3 Knowledge

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.

9.4 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.

 

10 Case 2: Studying bird migration's behavior

10.1 Data

The data is the original observation record of bird migration's behavior. These data include:

The tag number of each bird

Migration start and end time

Migration path (recorded by GPS)

Daily flight distance

Meteorological conditions (temperature, wind speed, precipitation, etc.)

These data are original observation records and have not been processed or interpreted.

10.2 Information

Information is the processing and interpretation of data, which semantically associates data with existing cognitive objects through a specific purpose to identify and classify different semantics. The steps of processing data generation information include:

Draw a chart of the daily migration path

Calculate the average daily flight speed.

Analyze the change of flight distance under different meteorological conditions

Through these processes, the information we obtained includes:

Average daily flight speed of birds

Migration behavior patterns of birds under different meteorological conditions

Geographical distribution of bird migration route

10.3 Knowledge

Knowledge is a systematic understanding and explanation of bird migration's behavior through high-order cognitive activities of information and semantic integrity abstraction activities with the help of assumptions. The steps of generating knowledge include:

Observe and learn:

Observe the chart of bird migration path and learn its basic pattern.

Learn the changes of bird flight distance under different meteorological conditions.

Hypothetical formation:

Suppose: "Birds migrate slowly under headwind conditions."

Suppose: "Birds will choose a path with less precipitation during migration."

Semantic integrity abstraction:

Give the above assumptions "complete" semantics and abstract them through higher-order cognitive activities. For example, suppose that "all birds migrate slowly under headwind conditions".

Semantic connection construction:

The new knowledge semantics (bird migration behavior pattern) is connected with the existing cognitive content (the influence of meteorological conditions on flight) to form a systematic knowledge structure.

Knowledge verification and correction:

Through further observation and experiment, the correctness and effectiveness of the hypothesis are verified. For example, the influence of headwind conditions on migration speed is verified by migration records in different time periods and different meteorological conditions.

According to the new observation results and experimental data, the existing knowledge system is revised and improved.

10.4 Detailed explanation of specific case steps

Observe and learn:

Through observation and data collection, researchers found that different birds showed certain behavior patterns during migration. For example, under certain wind speed and precipitation conditions, the migration path and speed of birds will change.

Hypothetical formation:

The researchers put forward hypotheses based on the observation results. For example, suppose that "the migration speed of birds is slow under headwind conditions" is based on the phenomenon that birds slow down under headwind conditions observed many times.

Semantic integrity abstraction:

By abstracting some observation results, the researcher endows the hypothesis with "complete" semantics and forms a systematic understanding. For example, by analyzing the migration speed of different birds under different wind speeds, the knowledge that "all birds migrate slowly under headwind conditions" is abstracted.

Semantic connection construction:

Researchers link new knowledge with existing cognitive content to form a systematic knowledge structure. For example, bird migration's behavior pattern is related to the influence of meteorological conditions to form a systematic understanding of bird migration's behavior.

Knowledge verification and correction:

Through further observation and experiment, the researcher verifies the correctness and validity of the hypothesis. For example, the influence of headwind conditions on migration speed is verified by migration records in different time periods and different meteorological conditions. If the new observation results are inconsistent with the hypothesis, researchers need to revise the hypothesis and improve the knowledge system.

10.5 Mathematical representation of knowledge

In this case, the mathematical representation of knowledge semantics can help us understand its integrity and structure. The semantic attribute set s of knowledge concept is expressed as:

S={f1,f2,,fn}

Among them, fi represents a semantic feature of knowledge concept. In this case, semantic features may include:

f1: Migration speed

f2: Wind speed condition

f3: Precipitation conditions

f4: Migration path

The knowledge concept set K contains all instances that share a complete semantic attribute set:

K={kk sharing S}

The process of generating knowledge can be expressed as:

K:XY

Among them, X represents the set or combination of data semantics, information semantics, knowledge semantics, wisdom semantics and purpose semantics (that is, DIKWP content semantics), and Y represents the generated new knowledge semantic association.

10.6 Philosophical significance of knowledge

In this case, knowledge is not only an observation record of bird migration's behavior, 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 explanation of bird migration's behavior. Through hypothesis and abstraction, researchers can reveal the deep connection and internal logic between phenomena and provide a more comprehensive and profound understanding of bird migration's behavior.

We can see how data, information and knowledge form a systematic understanding process from the original observation records through the higher-order cognitive activities of cognitive subjects in DIKWP model. 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 process shows the important role of knowledge in cognition and understanding the world.

 

11 Comparative analysis

11.1 Professor Yucong Duan's definition of knowledge

Professor Yucong Duan's definition of knowledge emphasizes the following key points:

Semantic integrity: the semantics of knowledge concepts correspond to one or more "complete" semantics in cognitive space.

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.

11.2 Compare other major knowledge definitions and models

11.2.1 DIKW model (data-information-knowledge-wisdom)

DIKW model is a classic model in the field of knowledge management, which divides knowledge into four levels: data, information, knowledge and wisdom.

Knowledge definition:

Knowledge is processed and understood information, which can be used for decision-making and action.

Knowledge is the further explanation and understanding of information, which usually includes pattern recognition, relationship and rule formation.

Knowledge is regarded as static and structured information in DIKW model.

Key features:

Hierarchical: data, information, knowledge and wisdom are gradually rising levels, and each level contains the processing and understanding of the previous level.

Static: knowledge is mainly regarded as static structured information, emphasizing the storage and management of information.

Contrast:

Professor Yucong Duan's definition emphasizes the dynamic and semantic integrity of knowledge, which is formed through cognitive activities and has a higher abstract category.

DIKW model pays more attention to the category and storage of knowledge, while Professor Yucong Duan's definition pays more attention to the process of knowledge generation and verification.

11.2.2 SECI Model of Nonaka and Takeuchi (Socialization, Externalization, Combination, Internalization)

SECI model is a dynamic model in knowledge management, which describes the transformation process of knowledge in an organization.

Knowledge definition:

Knowledge is divided into explicit knowledge and tacit knowledge.

Explicit knowledge can be transmitted by writing and coding, while tacit knowledge is personal experience and insight, which is difficult to transmit directly.

Knowledge is transformed through four processes: socialization (recessive to recessive), externalization (recessive to dominant), combination (dominant to dominant) and internalization (dominant to recessive).

Key features:

Dynamic: emphasizing the dynamic transformation and sharing process of knowledge in the organization.

Duality: the mutual transformation between explicit knowledge and tacit knowledge.

Contrast:

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.

Knowledge transformation in SECI model emphasizes the interaction between implicit and explicit knowledge, while Professor Yucong Duan's definition mainly focuses on the abstraction and verification of knowledge.

11.2.3 Polanyi's tacit knowledge theory

The theory of tacit knowledge put forward by Michael Polanyi emphasizes the importance of tacit knowledge, that is, knowledge that is difficult to formalize and transmit.

Knowledge definition:

Knowledge is divided into explicit knowledge and tacit knowledge.

Tacit knowledge is an inexpressible personal experience and skill.

Key features:

Difficult to formalize: Tacit knowledge is difficult to transfer through language or document.

Individuality: Tacit knowledge is deeply rooted in personal experience and skills.

Contrast:

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 Yucong Duan's definition also considers the sharing and dissemination of knowledge at the social level.

11.2.4 Cynefin framework

Cynefin framework is a model for decision-making, which describes different types of knowledge and problems.

Knowledge definition:

Knowledge is applied in different situations in different ways, and the framework is divided into five domains: simple, complex, complex, chaotic and disorderly.

The application and decision-making methods of knowledge in each domain are different.

Key features:

Situational: the application and decision-making of knowledge changes according to different situations.

Diversity: Emphasize that different types of problems need different knowledge processing methods.

Contrast:

The framework of Cynefin emphasizes the application and decision-making of knowledge in different situations, while Professor Yucong Duan's definition pays more attention to the generation, verification and structuring of knowledge.

The framework of Cynefin is suitable for the decision-making of complex systems and problems, while Professor Yucong Duan's definition is suitable for the theoretical construction and semantic understanding of knowledge.

11.3 Comprehensive analysis

Dynamic and static: Professor Yucong Duan's definition emphasizes the dynamic generation and verification process of knowledge, and knowledge is formed through the higher-order cognitive activities of cognitive subjects. In contrast, DIKW model emphasizes the static hierarchy and storage of knowledge.

Semantic integrity and hierarchy: Professor Yucong Duan's definition pays attention to the semantic integrity and abstraction of knowledge, while DIKW model and SECI model pay more attention to the hierarchy and transformation process of knowledge.

Individuality and sociality: Polanyi's tacit knowledge theory emphasizes the individuality of knowledge, personal experience and skills that are difficult to transmit. Professor Yucong Duan's definition considers the sharing and dissemination of knowledge at the individual and social levels.

Situational and Structured: The Cynefin framework focuses on the application and decision-making of knowledge in different situations, and emphasizes the influence of situations on knowledge application. Professor Yucong Duan's definition emphasizes the structure of knowledge and the understanding of complex systems.

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.

The following is a detailed comparative analysis of the knowledge definition proposed by Professor Yucong Duan and other major knowledge definitions and models in tabular form:

 

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.

 

 

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.

 

Conclusion

We can see how data, information and knowledge form a systematic understanding process from the original observation records through the higher-order cognitive activities of cognitive subjects in DIKWP model. 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 process shows the important role of knowledge in cognition and understanding the world.

 

 

References

 

[1] Nonaka, I., & Takeuchi, H. (1995). The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press.

[2] Polanyi, M. (1966). The Tacit Dimension. Doubleday & Company.

[3] Davenport, T. H., & Prusak, L. (1998). Working Knowledge: How Organizations Manage What They Know. Harvard Business School Press.

[4] Alavi, M., & Leidner, D. E. (2001). Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues. MIS Quarterly, 25(1), 107-136.

[5] Choo, C. W. (1998). The Knowing Organization: How Organizations Use Information to Construct Meaning, Create Knowledge, and Make Decisions. Oxford University Press.

[6] Grant, R. M. (1996). Toward a Knowledge-Based Theory of the Firm. Strategic Management Journal, 17(S2), 109-122.

[7] Nonaka, I. (1994). A Dynamic Theory of Organizational Knowledge Creation. Organization Science, 5(1), 14-37.

[8] Blackler, F. (1995). Knowledge, Knowledge Work and Organizations: An Overview and Interpretation. Organization Studies, 16(6), 1021-1046.

[9] Spender, J.-C. (1996). Making Knowledge the Basis of a Dynamic Theory of the Firm. Strategic Management Journal, 17(S2), 45-62.

[10] Teece, D. J. (1998). Capturing Value from Knowledge Assets: The New Economy, Markets for Know-How, and Intangible Assets. California Management Review, 40(3), 55-79.

[11] Tsoukas, H., & Vladimirou, E. (2001). What is Organizational Knowledge? Journal of Management Studies, 38(7), 973-993.

[12] Dretske, F. I. (1981). Knowledge and the Flow of Information. MIT Press.

[13] Zack, M. H. (1999). Managing Codified Knowledge. Sloan Management Review, 40(4), 45-58.

[14] Brown, J. S., & Duguid, P. (1991). Organizational Learning and Communities of Practice: Toward a Unified View of Working, Learning, and Innovation. Organization Science, 2(1), 40-57.

[15] Schultze, U., & Leidner, D. E. (2002). Studying Knowledge Management in Information Systems Research: Discourses and Theoretical Assumptions. MIS Quarterly, 26(3), 213-242.

[16] Boisot, M. (1998). Knowledge Assets: Securing Competitive Advantage in the Information Economy. Oxford University Press.

[17] Kogut, B., & Zander, U. (1992). Knowledge of the Firm, Combinative Capabilities, and the Replication of Technology. Organization Science, 3(3), 383-397.

[18] Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity. Cambridge University Press.

[19] Hansen, M. T., Nohria, N., & Tierney, T. (1999). Whats Your Strategy for Managing Knowledge? Harvard Business Review, 77(2), 106-116.

[20] von Krogh, G., Ichijo, K., & Nonaka, I. (2000). Enabling Knowledge Creation: How to Unlock the Mystery of Tacit Knowledge and Release the Power of Innovation. Oxford University Press.

[21] Alavi, M., & Leidner, D. E. (1999). Knowledge Management Systems: Issues, Challenges, and Benefits. Communications of the AIS, 1(2es), 1.

[22] Cook, S. D. N., & Brown, J. S. (1999). Bridging Epistemologies: The Generative Dance Between Organizational Knowledge and Organizational Knowing. Organization Science, 10(4), 381-400.

[23] Cross, R., & Sproull, L. (2004). More than an Answer: Information Relationships for Actionable Knowledge. Organization Science, 15(4), 446-462.

[24] Simon, H. A. (1991). Bounded Rationality and Organizational Learning. Organization Science, 2(1), 125-134.

[25] Leonard-Barton, D. (1995). Wellsprings of Knowledge: Building and Sustaining the Sources of Innovation. Harvard Business School Press.

[26] Nonaka, I., von Krogh, G., & Voelpel, S. (2006). Organizational Knowledge Creation Theory: Evolutionary Paths and Future Advances. Organization Studies, 27(8), 1179-1208.

[27] McDermott, R. (1999). Why Information Technology Inspired but Cannot Deliver Knowledge Management. California Management Review, 41(4), 103-117.

[28] Earl, M. (2001). Knowledge Management Strategies: Toward a Taxonomy. Journal of Management Information Systems, 18(1), 215-233.

[29] Argyris, C., & Schön, D. A. (1978). Organizational Learning: A Theory of Action Perspective. Addison-Wesley.

[30] Senge, P. M. (1990). The Fifth Discipline: The Art and Practice of the Learning Organization. Doubleday/Currency.

 

 



https://blog.sciencenet.cn/blog-3429562-1435097.html

上一篇:段玉聪教授提出知识的定义(概念和语义)-DIKWP-Data
下一篇:段玉聪教授的DIKWP模型:数据、信息、知识的定义
收藏 IP: 140.240.38.*| 热度|

0

该博文允许注册用户评论 请点击登录 评论 (0 个评论)

数据加载中...

Archiver|手机版|科学网 ( 京ICP备07017567号-12 )

GMT+8, 2024-6-21 04:52

Powered by ScienceNet.cn

Copyright © 2007- 中国科学报社

返回顶部