段玉聪
Information Definition and Processing for DIKWP Models
2024-5-15 21:47
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Yucong Duan Proposes Information Definition and Processing for DIKWP Models

 

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

Abstract

Introduction

1 Professor Yucong Duan's information definition

2 Definition and processing of DIKWP-Information

2.1 Concept definition

2.2 Key point analysis

2.2.1 Purpose driven information generation

2.2.2 The formation of semantic associations

2.2.3 Dynamicity and constructiveness

3 The process of generating information semantics

3.1 Mathematical representation of information semantics

3.2 Specific example analysis

3.2.1 Medical diagnostic system

3.2.2 Autopilot

4 The expression of information semantics in cognition

4.1 Information semantics generate new semantic associations through recombination and transformation

4.2 The application of information semantics in cognitive science and AI

4.2.1 Conceptual integration theory and metaphor theory

4.2.2 Implementation of information semantics in AI

5 The philosophical significance of information semantics

5.1 The constructive nature of information

5.2 The diversity and depth of information semantics

6 Case: intelligent medical diagnosis system

6.1 Scene description

6.2 Information generation process

6.2.1 Step 1: Data collection and preliminary recording

6.2.2 Step 2: Semantic matching and concept confirmation

6.2.3 Step 3: Dynamic generation of information semantics

6.3 Detailed analysis: specific diagnostic process

6.3.1 Specific example 1: Diagnosis of hypertension

6.3.2 Specific example 2: Diagnosis of diabetes

6.4 The dynamicity and constructiveness of information semantics

6.4.1 The diversity and depth of information semantics

6.4.2 Philosophical significance

7 Related work

7.1 The DIKW model in information science

7.2 Semantic web and ontology

7.3 Semantic memory in cognitive science

7.4 Knowledge representation and inference in artificial intelligence

7.5 Semantic analysis in natural language processing

8 Comparative analysis

8.1 The information definition in professor Yucong Duan's DIKWP model

8.2 Information definition in the DIKW model

8.3 The definition of information in semantic web and ontology

8.4 The definition of information in cognitive science

8.5 Information definition in artificial intelligence

8.6 Comprehensive comparative analysis

9 Detailed comparative analysis

9.1 Information definition

9.2 Semantic processing

9.3 Subjectivity

9.4 Context dependency

9.5 Dynamicity and constructiveness

9.6 Mathematical representation of information

Conclusion

References

Abstract

Professor Yucong Duan proposed the definition of information in the DIKWP model, emphasizing the specific manifestations of "different" semantics expressed by information in the cognitive process. Information semantics are formed through semantic associations and probability confirmation driven by specific objectives. This definition differs significantly from the information definitions in traditional DIKW models, semantic web and ontology, cognitive science, and artificial intelligence. This article compares and analyzes the information definitions in various models in detail, exploring their similarities and differences in semantic processing, subjectivity, context dependency, and dynamic generation, in order to deepen the understanding of information processing and cognitive processes, and provide a theoretical basis for research in related fields.

Thanks to Professor Xifan Yao, Professor Erxiang Dou, Professor Binxiang Jiang, Professor Yanfei Liu, etc

 

Introduction

In the fields of information science, cognitive science, and artificial intelligence, the definition and processing methods of information are crucial for understanding the process of data transformation, information transmission, and knowledge generation. The traditional DIKW model views information as processed and meaningful data, emphasizing the objective transformation process from data to information. The semantic web and ontology define information through semantic tags and ontology, achieving interoperability and standardization. Cognitive science focuses on the storage and processing of information in the brain, emphasizing the differences in individual cognitive structures. Artificial intelligence uses information as the fundamental entity for training models and reasoning, representing information through feature vectors.

Professor Yucong Duan proposed a new definition of information in the DIKWP model, emphasizing that information forms new information semantics through specific purpose driven semantic associations and probabilistic confirmations in the cognitive process. This article aims to provide a detailed comparative analysis of the information definitions in the DIKWP model and traditional information models, exploring their similarities and differences in semantic processing, subjectivity, context dependency, and dynamic generation, and demonstrating the value of this new definition in practical applications.

 

1 Professor Yucong Duan's information definition

DIKWP-Information, as a concept, corresponds to one or more "different" semantics in cognition. The information semantics of information concepts refer to the semantic association between the DIKWP cognitive object in the cognitive space of the cognitive subject and the DIKWP cognitive object already recognized by the cognitive subject through specific purpose concepts or purpose semantics in the semantic space. With the help of the cognitive subject's cognitive purpose, the same cognition (corresponding to data semantics) or different cognition is formed in the cognitive space. The information semantics are formed by the probability confirmation or logical judgment confirmation of "different" semantics in the semantic space, or new semantic associations are generated in the semantic space ("new" is a type of "different" semantics). When processing information concepts or information semantics, cognitive processing will identify the differences between the input data, information, knowledge, wisdom or purpose, and the recognized DIKWP cognitive object, corresponding to various different semantics, and classify the information. For example, in cognitive space, when facing a parking lot, although all cars in the parking lot can be cognitively classified as "cars", the parking location, parking time, wear and tear, owner, function, payment records, and experience of each car represent cognitive differences driven by different cognitive objectives in semantic space, ultimately corresponding to different information semantics. The various different semantics corresponding to information objects often exist in the cognition of cognitive subjects and are often not explicitly expressed. For example, patients with depression may use the concept of "low mood" to express the negative degree of current emotions in their cognitive space compared to their past emotions. The cognitive subject selects the concept of "low" in their conceptual space to reflect the semantic meaning of the target information to be expressed in their cognitive state confirmation. However, due to the fact that the semantic interpretation of the concept of "low" in the cognitive space of the communication object may not necessarily be the same as the semantic meaning of the cognitive subject's information, or there may be different semantics, it is impossible to objectively perceive the semantic meaning of the information to the communication object, and thus the semantic meaning of the information becomes the subjective cognitive semantic meaning of the cognitive subject.

The mathematical representation of information semantic processing: Information semantics in the DIKWP model correspond to new associated semantics generated by specific purpose driven processing of data semantics, information semantics, knowledge semantics, Wisdom semantics, and purpose semantics. In semantic space, using purpose to drive information semantic mapping of a set of inputs X to a new semantic association Y:

I: X Y, where X represents the set or combination of data semantics, information semantics, knowledge semantics, wisdom semantics, and purpose semantics (i.e. DIKWP content semantics), and Y represents the newly generated DIKWP content semantic association. This mapping emphasizes the dynamic and constructive nature of the information semantic generation process.

Information semantics correspond to the expression of various different semantics in cognition in the DIKWP model. By leveraging the cognitive purpose of the cognitive subject, information semantics generate new semantic associations by linking the semantics corresponding to data, information, knowledge, wisdom, or purpose with the existing cognitive objects of the cognitive subject. In cognitive space, this process not only involves the re semantic combination and semantic transformation of known DIKWP content (including semantic connectivity to form so-called cognitive understanding), but also involves the dynamic process of generating new DIKWP cognitive semantics and continuously forming cognitive understanding through this re combination and transformation.

The generation of information semantics is about how to connect different sets or combinations of data semantics, information semantics, knowledge semantics, wisdom semantics, or purpose semantics through a specific purpose of the cognitive subject, in order to confirm and achieve the so-called cognitive understanding in the cognitive space of the cognitive subject. Correspondingly, the cognitive subject forms semantic associations, supplements, and judgments for broken, missing, or uncertain semantic connections in the semantic space, using the generated information semantics to achieve the purpose of eliminating cognitive uncertainty from semantic uncertainty. This process involves associating, comparing, and conceptualizing the observed phenomena or cognitive input content with existing DIKWP content in the semantic space through cognitive space, and then using certain different semantics to recognize and classify new DIKWP content. In AI, this can correspond to cognitive understanding in explaining and processing the relationship between DIKWP content, such as analyzing the correlation between DIKWP content through algorithms to extract valuable information semantics.

Information semantic processing is a dynamic cognitive process that focuses on how to connect the semantic content of DIKWP with the existing cognitive object of the cognitive subject through the subjective purpose of the cognitive subject, thereby generating valuable semantic associations. The value of information lies in becoming a bridge connecting data, information, knowledge, wisdom, and purpose, revealing the cognitive subject's understanding of the semantics of DIKWP content.

In cognitive science, semantic processing of information can be further explained through various cognitive theories, such as conceptual integration theory, which explains how information from different sources can be fused together to form new meanings and understandings. For example, by combining a person's behavior (DIKWP content semantics) with specific contextual information, the cognitive subject can have a clearer understanding of the purpose of that behavior.

The semantic correlation of information is related to the Metaphor Theory and Blending Theory in cognitive linguistics, which study how to create new meanings through linguistic metaphors and conceptual integration. In AI systems, this involves designing algorithms to simulate how humans construct new cognitive models through existing DIKWP content semantics.

The process of generating information semantics is the result of the semantic interaction between DIKWP content semantics and DIKWP content semantics, that is, DIKWP*DIKWP semantics. This process not only involves the reorganization or reinterpretation of DIKWP content semantics, but also is a dynamic, purpose driven cognitive activity. Through this activity, cognitive subjects can recognize and understand new patterns and associations, thereby expanding their cognitive boundaries. The generation of information semantics is constructive and dynamic, and in layman's terms, it is generated through data interpretation and semantic connection.

Information is viewed in philosophy as the organization and interpretation of DIKWP content, generating new meanings (semantics) by constructing semantic relationships between DIKWP content. Through the process of information semantic processing, cognitive subjects are able to recognize and understand the connections and differences between phenomena. The generation of information semantics involves the active participation of cognitive subjects, which is an action of semantically processing DIKWP content, reflecting the subjective interpretation of the real world by cognitive subjects. The philosophical significance of information as an expression of different semantics in cognition lies in the fact that the process of information generation and processing is essentially an understanding and comprehension of the diversity and complexity of the world. Information semantics is not only the aggregation or reorganization of DIKWP content semantics, but also the creation of a new semantic association, reflecting the active exploration and interpretation of the world by cognitive subjects. This process of explanation involves the exploration of deeper connections and internal logic of phenomena, which is a pursuit of a deeper understanding of the world and a transfer to another deeper level of understanding.

The construction properties of information semantics:

The generation and understanding of information is not a passive receiving process, but an active construction process. The semantic information relies on existing DIKWP content and purpose driven cognitive frameworks. This viewpoint is consistent with Kant's epistemology, which states that the cognitive subject's understanding of the world is constructed through an internal perceptual framework and prior concepts. The value of information lies in its ability to expand or reconstruct our cognitive framework, thereby enhancing our understanding of the world.

The diversity and depth of information semantics:

The information processing in DIKWP goes beyond simple data aggregation and instead focuses on the dynamic relationships between data, information, knowledge, wisdom or purpose, and the generation of new semantic associations. This process reflects Heraclitus's theory of rheology - that everything flows and nothing remains constant. The value of information lies in its fluidity and the changes it can cause, rather than static factual records. Information has become a link connecting different cognitive states, driving cognitive subjects from one understanding state to another.

The dynamism and cognitive structure of information:

In the definition of information, the DIKWP model emphasizes the role of information as a bridge connecting different semantic entities. This corresponds to Deleuze's theory of "difference and repetition". In Deleuze's view, the process of cognition is carried out by identifying the differences between things, and this process is the core of information processing. Information not only includes the semantic differences of DIKWP content, but also connects with existing knowledge structures through these differences, creating new knowledge. This dynamic process of updating cognitive structures is crucial for cognitive development and knowledge growth.

 

2 Definition and processing of DIKWP-Information

2.1 Concept definition

In the DIKWP model, information is defined as the specific manifestation of one or more "different" semantics expressed in cognition. The core of information concept lies in the semantic association between cognitive objects in the cognitive space of cognitive subjects and known cognitive objects in the semantic space through specific purpose concepts or purpose semantics. This process forms the same cognition (corresponding to data semantics) or different cognition in the cognitive space through the cognitive objective of the cognitive subject. Through differential cognition, information semantics are formed through probabilistic confirmation or logical judgment of "different" semantics in the semantic space, or new semantic associations are generated in the semantic space.

2.2 Key point analysis

2.2.1 Purpose driven information generation

The process of information generation is driven by the purpose of the cognitive subject. This means that cognitive subjects have specific cognitive goals and needs in specific contexts, which determine the direction and content of information generation. For example, when a doctor reviews a patient's medical record, their purpose is to diagnose the condition. Therefore, various data in the medical record (such as the patient's temperature, blood pressure, symptom description, etc.) are transformed into meaningful information in the doctor's cognitive space, helping the doctor make a diagnosis.

2.2.2 The formation of semantic associations

The formation of information relies on semantic association, which means that new data is associated with existing cognitive objects in the semantic space. This process requires cognitive agents to achieve it through semantic matching and conceptual confirmation. For example, autonomous vehicle need to associate sensor data with known traffic rules, road conditions and vehicle dynamics to form the information needed for driving decisions.

2.2.3 Dynamicity and constructiveness

The process of generating information is dynamic and constructive. The cognitive subject constantly generates and updates information dynamically based on new inputs and existing cognitive backgrounds. For example, in financial markets, traders constantly receive market data (such as stock prices, trading volume, news, etc.) and dynamically transform this data into information that helps decision-making to adapt to rapidly changing market environments.

 

3 The process of generating information semantics

3.1 Mathematical representation of information semantics

In the DIKWP model, information semantics are new associative semantics generated through specific purpose driven processing. In the semantic space, purpose drives information semantics I to map input X to a new semantic association Y: I: XY, where X represents the collection or combination of data semantics, information semantics, knowledge semantics, Wisdom semantics, and purpose semantics, and Y represents the generated new DIKWP content semantic association. This mapping process emphasizes the dynamic and constructive nature of information semantic generation.

3.2 Specific example analysis

3.2.1 Medical diagnostic system

Input: Patient's physical examination data (data semantics), medical history records (information semantics), medical knowledge base (knowledge semantics), doctor's diagnostic experience (Wisdom semantics), current diagnostic objectives (purpose semantics).

Mapping process: The system processes these inputs through purpose driven processing to generate diagnostic reports. This process includes identifying differences between patient data and known disease characteristics, matching relevant medical knowledge, and making judgments based on the doctor's experience.

Output: Generate new diagnostic information (new semantic association), such as "Patient may have X disease and needs further examination of Y item".

3.2.2 Autopilot

Input: real-time camera images (data semantics), road maps (information semantics), traffic regulations (knowledge semantics), driving strategies of the system (Wisdom semantics), and current driving targets (purpose semantics).

Mapping process: The system comprehensively processes these inputs to generate driving instructions. This process includes identifying road signs, detecting pedestrians and vehicles, following traffic rules, and optimizing driving paths.

Output: Generate new driving decision information, such as "Turn right 200 meters ahead and avoid congested road sections.".

 

4 The expression of information semantics in cognition

4.1 Information semantics generate new semantic associations through recombination and transformation

Information semantics generate new semantic associations by linking the semantics of data, information, knowledge, wisdom or purpose with the existing cognitive objects of cognitive subjects. In cognitive space, this process involves the semantic recombination and transformation of known DIKWP content (including semantic connectivity to form cognitive understanding), as well as the dynamic process of generating new cognitive semantics and continuously forming cognitive understanding through recombination and transformation.

Examples of cognitive processing of information

Information processing identifies the differences between the input data, information, knowledge, wisdom, or purpose and the recognized DIKWP cognitive object, corresponding to various semantics, and classifies the information. For example, in cognitive space, the parking location, time, degree of wear and tear, owner, function, etc. of each car in a parking lot are all different semantics, which form various information in the cognitive process.

4.2 The application of information semantics in cognitive science and AI

4.2.1 Conceptual integration theory and metaphor theory

Information semantic processing can be further explained through conceptual integration theory and metaphor theory. For example, conceptual integration theory explains how to form new meanings and understandings by fusing information from different sources. Metaphor theory studies the creation of new meanings through linguistic metaphors and conceptual integration.

Conceptual Integration Theory:

This theory studies how cognitive subjects integrate information from different sources to form a new conceptual framework. For example, when doctors combine the patient's physical examination data and medical knowledge for diagnosis, the diagnostic conclusion formed is the result of conceptual integration.

Metaphor theory:

This theoretical study maps concepts from one domain to another through metaphor, thereby generating new meanings. For example, in market analysis, the volatility of the stock market is likened to "tides", which helps traders understand market dynamics.

4.2.2 Implementation of information semantics in AI

In AI systems, information semantic processing involves designing algorithms to simulate human cognitive processes. For example, by analyzing the correlation between DIKWP content, valuable information semantics can be extracted to form a new cognitive model.

Natural Language Processing (NLP):

In NLP, AI systems extract information from text through semantic analysis techniques and associate it with a knowledge base. For example, semantic networks and ontology technologies can help AI systems understand and generate implicit information in natural language.

Machine learning:

Machine learning algorithms can recognize complex correlations between large amounts of data through training. For example, in a recommendation system, the algorithm generates personalized recommendation information based on the user's historical behavioral data (such as browsing history, purchasing history).

Knowledge Graph:

A knowledge graph integrates information from different sources through semantic associations to form a knowledge network. For example, Google's knowledge graph generates rich search results by linking different webpage contents.

 

5 The philosophical significance of information semantics

5.1 The constructive nature of information

The generation and understanding of information is not a passive receiving process, but an active construction process. The semantic information relies on existing DIKWP content and purpose driven cognitive frameworks. This viewpoint is consistent with Kant's epistemology, which states that the cognitive subject's understanding of the world is constructed through an internal perceptual framework and prior concepts. The value of information lies in its ability to expand or reconstruct our cognitive framework, thereby enhancing our understanding of the world.

5.2 The diversity and depth of information semantics

The information processing in DIKWP goes beyond simple data aggregation and instead focuses on the dynamic relationships between data, information, knowledge, wisdom or purpose, and the generation of new semantic associations. This process reflects Heraclitus's theory of rheology - that everything flows and nothing remains constant. The value of information lies in its fluidity and the changes it can cause, rather than static factual records. Information has become a link connecting different cognitive states, driving cognitive subjects from one understanding state to another.

Through in-depth exploration of information definition and semantic processing in Professor Yucong Duan's DIKWP model, we have found that information semantics is not only the aggregation or reorganization of data, but also a dynamic, purpose driven cognitive process. This process forms new semantic associations through semantic matching and conceptual confirmation of cognitive subjects, promoting the deepening and expansion of cognitive understanding. The generation and processing of information reflects the active exploration and interpretation of the world by cognitive subjects, and is a reflection of their understanding of the diversity and complexity of the world.

This model provides a new theoretical framework and practical guidance for cognitive science, natural language processing, artificial intelligence and other fields. Future research can further explore the practical application and optimization of the DIKWP model in specific applications, promoting the development of information processing technology.

 

6 Case: intelligent medical diagnosis system

6.1 Scene description

In intelligent medical diagnosis systems, doctors use AI technology to assist in diagnosing the patient's condition. The system collects various data of patients (such as physical examination data and medical history records), and combines the professional knowledge of doctors and medical knowledge base for comprehensive analysis to generate diagnostic information. The following will provide a detailed introduction to the generation and processing of information in the system, showcasing the application of information definition and semantic processing in the DIKWP model.

6.2 Information generation process

6.2.1 Step 1: Data collection and preliminary recording

The system first collects patient data from different sources:

Physical examination data: such as blood pressure, body temperature, blood sugar levels, electrocardiogram, etc.

Medical history records: such as past illnesses, surgical records, medication history, etc.

These data are the basic inputs of the system and belong to the data category of the DIKWP model.

6.2.2 Step 2: Semantic matching and concept confirmation

The system will match the collected data with the existing medical knowledge base for semantic matching and concept confirmation:

Medical examination data matching: The system uses semantic matching technology to compare medical examination data (such as blood pressure) with the normal value range and determine whether it is abnormal.

Medical history matching: The system matches medical history records with known disease models to identify potential health risks.

In this process, the system performs semantic classification and matching on the data, forming preliminary information semantics.

6.2.3 Step 3: Dynamic generation of information semantics

The system dynamically generates diagnostic information based on the doctor's diagnosis purpose, combined with data semantics, information semantics, knowledge semantics, and Wisdom semantics:

Doctor's diagnosis purpose: The doctor's purpose is to diagnose the patient's condition, identify the cause, and develop a treatment plan.

Semantic association and generation:

The system semantically associates the patient's physical examination data with the disease features in the medical knowledge base.

Combining the professional knowledge and experience of doctors, conduct in-depth analysis of data and generate diagnostic reports.

The system maps input X (including a set of data semantics, information semantics, knowledge semantics, wisdom semantics, and purpose semantics) to a new semantic association Y through a specific purpose driven information semantics I: I: X Y, where X is medical examination data, medical history records, medical knowledge base, and professional knowledge of medical students, and Y is the generated diagnostic information.

6.3 Detailed analysis: specific diagnostic process

6.3.1 Specific example 1: Diagnosis of hypertension

Data collection:

Physical examination data: The blood pressure reading is 150/95 mmHg (higher than normal).

Medical history record: The patient has a family history of hypertension.

Semantic matching:

The system compared the blood pressure data with the normal blood pressure range and found abnormal readings.

Match medical history records with known risk factors for hypertension to confirm the patient's risk of hypertension.

Information semantic generation:

Semantic association: Semantic association between abnormal blood pressure data and diagnostic criteria for hypertension.

Output information: Generate preliminary diagnostic information by combining family medical history and other physical examination data.

The doctor's diagnosis purpose: to confirm whether the patient has hypertension and make a treatment plan.

Diagnostic information: The patient may have hypertension, further blood pressure monitoring and lifestyle adjustments are recommended, and medication treatment may be required.

6.3.2 Specific example 2: Diagnosis of diabetes

Data collection:

Physical examination data: The fasting blood glucose level is 8.5 mmol/L (higher than normal).

Medical history record: The patient is overweight and has a family history of diabetes.

Semantic matching:

The system compared the blood glucose data with the normal blood glucose range and found abnormal readings.

Match the medical history record with the known risk factors of diabetes to confirm that the patient has diabetes risk.

Information semantic generation:

Semantic association: semantic association between abnormal blood glucose data and diagnostic criteria of diabetes.

Output information: Based on factors such as overweight and family medical history, generate preliminary diagnostic information.

The doctor's diagnosis purpose: to confirm whether the patient has diabetes and make a treatment plan.

Diagnostic information: The patient may have diabetes, and further glucose tolerance test and lifestyle adjustment are recommended, and drug treatment may be required.

6.4 The dynamicity and constructiveness of information semantics

6.4.1 The diversity and depth of information semantics

Information semantics is not only the aggregation or reorganization of data, but also the dynamic generation of new semantic associations driven by the purpose of cognitive subjects. For example:

The nature of information construction: In intelligent medical diagnosis systems, doctors construct meaningful diagnostic information by combining patient physical examination data and medical knowledge. This process is dynamic, and with the introduction of new data and knowledge, diagnostic information will continue to be updated and optimized.

The dynamism of information: During the diagnostic process, the system continuously receives new data (such as new medical examination results) and dynamically adjusts diagnostic information. For example, with the continuous updating of patients' blood glucose monitoring data, the system will dynamically adjust the diagnosis and treatment plan of diabetes according to the latest data.

6.4.2 Philosophical significance

The process of generating and processing information reflects a philosophical understanding of the diversity and complexity of the world:

Kant's epistemology: The generation of information depends on the prior concepts and perceptual framework of the cognitive subject. The process of generating diagnostic information in intelligent medical diagnostic systems relies on the medical knowledge and diagnostic experience of doctors, which is a practical application of Kant's epistemology.

Heraclitus's Rheology: The value of information lies in its fluidity and variability. Diagnostic information constantly changes with the introduction of new data and knowledge, reflecting Heraclitus's theory of rheology.

Through the case of an intelligent medical diagnostic system, we have demonstrated in detail the application of information definition and semantic processing in Professor Yucong Duan's DIKWP model. This model emphasizes the subjectivity, dynamism, and constructiveness of information, and dynamically generates new semantic associations through the purpose of cognitive subjects. This process not only improves the efficiency and accuracy of information processing, but also provides new theoretical foundations and practical guidance for fields such as cognitive science, natural language processing, and artificial intelligence. Future research can further explore the practical application and optimization of the DIKWP model in other specific applications, promoting the development of information processing technology.

 

7 Related work

The following is a summary of some important related works, covering information definitions and processing methods in information science, semantic web, ontology, cognitive science, and artificial intelligence. These reviews provide important background for understanding the information concepts and semantics proposed by Professor Yucong Duan.

7.1 The DIKW model in information science

Main research

Ackoff, R. L. (1989). "From Data to Wisdom". Journal of Applied Systems Analysis.

Abstract: Akoff proposed the DIKW model, which describes the hierarchical relationship between data, information, knowledge, and wisdom. Data is unprocessed raw facts, information is processed data, knowledge is the meaningful structure of information, and Wisdom is the effective application of knowledge.

Contribution: laying the foundation for understanding data and information transformation processes in information science, emphasizing the role of each level in information processing.

Rowley, J. (2007). "The wisdom hierarchy: representations of the DIKW hierarchy". Journal of Information Science.

Abstract: Rowley provides a detailed overview of the DIKW model, exploring the characteristics and interrelationships of each level, and discussing the transformation process of information at different levels.

Contribution: Provided a comprehensive understanding of the DIKW model, emphasizing the role of information in knowledge management and organization.

7.2 Semantic web and ontology

Main research

Berners-Lee, T., Hendler, J., & Lassila, O. (2001). "The Semantic Web". Scientific American.

Absrtact: Berners Lee and others put forward the concept of semantic web, which aims to achieve interoperability and automatic processing of data on the Internet through standardized semantic tags and ontology definitions.

Contribution: laying the foundation for semantic web technology, emphasizing the interoperability of data through semantic linking and ontology.

Gruber, T. R. (1993). "A translation approach to portable ontology specifications". Knowledge Acquisition.

Abstract: Gruber proposed the concept of ontology and defined a framework of shared semantics for data exchange and understanding between different systems.

Contribution: Proposed principles and methods for building ontology, providing theoretical support for semantic web and knowledge engineering.

7.3 Semantic memory in cognitive science

Main research

Tulving, E. (1972). "Episodic and semantic memory". Organization of Memory.

Abstract: Tulving distinguishes situational memory from semantic memory and discusses the processing of semantic information in the brain.

Contribution: Revealed the importance of semantic memory in cognitive processes, providing a theoretical basis for understanding the storage and retrieval of information in the brain.

Collins, A. M., & Quillian, M. R. (1969). "Retrieval time from semantic memory". Journal of Verbal Learning and Verbal Behavior.

Abstract: Collins and Quillian proposed a semantic network model, which explains the organization and retrieval process of semantic information in the brain.

Contribution: Provided a model for semantic information processing in cognitive science, revealing the storage and retrieval mechanisms of information in cognitive structures.

7.4 Knowledge representation and inference in artificial intelligence

Main research

Brachman, R. J., & Levesque, H. J. (2004). Knowledge Representation and Reasoning.

Abstract: Brachman and Levesque comprehensively introduced the basic concepts and methods of knowledge representation and inference, and discussed how to represent and manipulate semantic information in AI systems.

Contribution: Provided a systematic theoretical framework for knowledge representation in the field of artificial intelligence, emphasizing the role of logic and reasoning in knowledge processing.

Russell, S. J., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach.

Abstract: The works of Russell and Norvig are classic textbooks in the field of AI, covering various aspects from data representation to advanced reasoning.

Contribution: Provides a comprehensive overview of AI technology, detailing knowledge representation and inference methods.

7.5 Semantic analysis in natural language processing

Main research

Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing.

Abstract: Manning and Sch ü tze introduced statistical methods in NLP and discussed how to understand text data through semantic analysis.

Contribution: It laid the foundation for statistical natural language processing and emphasized the importance of semantic analysis in text processing.

Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing.

Abstract: The works of Jurafsky and Martin are important textbooks in the field of NLP, covering various aspects from lexical semantics to text understanding.

Contribution: Provides a comprehensive overview of NLP technology, detailing the methods and applications of semantic processing.

By reviewing these important related works, we can gain a more comprehensive understanding of the theoretical background of information definition and semantic processing in the DIKWP model. These studies provide important theoretical support for semantic classification, conceptual confirmation, and cognitive processing of information, helping us understand the subjectivity and contextual dependence of information in the DIKWP model. If you need more detailed cases or further exploration in specific fields, please feel free to continue asking questions.

 

8 Comparative analysis

8.1 The information definition in professor Yucong Duan's DIKWP model

In the DIKWP model, information is defined as the specific manifestation of one or more "different" semantics expressed in cognition. The core of information concept lies in the semantic association between cognitive objects in the cognitive space of cognitive subjects and known cognitive objects in the semantic space through specific purpose concepts or purpose semantics. This process forms the same cognition (corresponding to data semantics) or different cognition in the cognitive space through the cognitive objective of the cognitive subject. Through differential cognition, information semantics are formed through probabilistic confirmation or logical judgment of "different" semantics in the semantic space, or new semantic associations are generated in the semantic space.

8.2 Information definition in the DIKW model

Definition: In the DIKW model, information is defined as processed and meaningful data. Data is raw, unprocessed facts and observations, while information is the result of processing, organizing, and structuring data to make it meaningful.

Key points:

Objectivity: Information is viewed as the result of data processing, emphasizing the process of transforming data into information.

Processing process: Information is generated through the processing and organization of data, usually including sorting, classification, summarization, etc.

Hierarchical relationship: Information is a higher-level level of data, and further processing of information can generate knowledge.

8.3 The definition of information in semantic web and ontology

Definition: In the semantic web and ontology, information is defined as entities with semantic labels that define the meaning and purpose of data. Through standardized semantic linking and ontology definition, achieve interoperability and automated processing of data.

Key points:

Semantic clarity: The semantics of information are clarified through ontology and standardized labels.

Interoperability: Emphasis is placed on achieving information interoperability between different systems through semantic linking and ontology.

Structured: Information is strictly defined as entities with clear semantics and structure.

8.4 The definition of information in cognitive science

Definition: In cognitive science, information is defined as the semantic and situational memory stored and processed by cognitive subjects. Information is a part of the semantic network and situational memory stored by cognitive subjects in the brain.

Key points:

Cognitive processing: Information is processed through the semantic memory and situational memory of the cognitive subject.

Storage and retrieval: Emphasize the process of storing and retrieving information in the brain.

Individual differences: The processing and storage of information depend on an individual's cognitive structure and experiential background.

8.5 Information definition in artificial intelligence

Definition: In artificial intelligence (AI), information is defined as the foundational entity used for training models and reasoning. Information is represented through feature vectors for model training, classification, and inference.

Key points:

Feature representation: Information is typically represented through feature vectors for computation and processing.

Model training: Information is the foundation for AI system training models and reasoning.

Automated processing: emphasizes the processing and application of information in automated systems.

8.6 Comprehensive comparative analysis

Feature/Model

DIKWP Model

DIKW Model

Semantic Web and Ontologies

Cognitive Science

Artificial Intelligence

Information Definition

Concrete manifestations of one or more "different" semantics in cognition, associated through purpose-driven semantic relations to cognitive subjects

Data processed and given meaning

Entities with semantic tags and structured definitions, achieving interoperability through ontologies

Semantic and situational memories stored and processed by cognitive subjects

Basic entities used for training models and inference, represented by feature vectors

Semantic Processing

Emphasizes the purpose and semantic associations of the cognitive subject, dynamically generating new information semantics

Meaning produced through processing and organizing data

Semantics defined by standardized ontologies and tags, achieving interoperability

Information processed through semantic and situational memories

Information represented by feature vectors for model training and inference

Subjectivity

High, generation of information depends on the purpose and background knowledge of the cognitive subject

Low, information seen as an objective result of data processing

Medium, semantics of information defined by standardized tags, but interpretation may vary

High, processing and storage of information depends on individual cognitive structures and experiences

Low, representation of information features is usually objective, though training data may be biased

Context Dependency

High, generation and meaning of information depend on the cognitive subject's purpose and context

Low, information lacks context dependency before processing

High, semantics of information depend on its position and relations within the ontology

High, semantics of information depend on the background and experiences of the cognizer

Medium, feature vectors of information may vary across different application scenarios

Dynamism and Constructiveness

High, the generation of information is dynamic and driven by purpose

Medium, information arises from static processing and organization of data

High, information continuously updated and expanded through semantic links and ontologies

High, storage and processing of information in cognitive structures are dynamic

High, information continuously updated and optimized through feature extraction and model training

Mathematical Representation of Information

Represented by purpose-driven semantic mappings of information, e.g., I:X→Y

No explicit mathematical representation, information is a result of data processing

Information entities represented by ontologies and semantic tags

Information represented by semantic networks and memory models

Information represented by feature vectors, used for computation and inference

 

9 Detailed comparative analysis

9.1 Information definition

The DIKWP model: Information is defined as the expression of "different" semantics in cognition, generated through the purpose and semantic association of the cognitive subject. Emphasize the subjectivity and dynamism of information.

DIKW model: Information is data that has been processed and given meaning. Emphasize the process and objectivity of converting data into information.

Semantic Web and Ontology: Information is an entity with semantic labels, and its semantics and structure are defined through ontology to achieve interoperability. Emphasize semantic clarity and structuring.

Cognitive science: Information is the semantic and situational memory stored and processed by cognitive subjects. Emphasize cognitive processing of information and individual differences.

Artificial intelligence: Information is the fundamental entity used for training models and reasoning, typically represented through feature vectors. Emphasize the feature representation and automated processing of information.

9.2 Semantic processing

DIKWP model: Information semantics are dynamically generated through the purpose and semantic association of cognitive subjects. Emphasize purpose driven and dynamic generation.

The DIKW model: Information generates meaning through data processing and organization. Emphasize the static processing process.

Semantic Web and Ontology: Information semantics achieve interoperability through standardized labels and ontology definitions. Emphasize semantic clarity and standardization.

Cognitive science: Information is processed through semantic memory and situational memory. Emphasize cognitive processes and individual differences.

Artificial intelligence: Information is represented through feature vectors for model training and inference. Emphasize computation and automation processing.

9.3 Subjectivity

The DIKWP model: The generation of information highly relies on the cognitive subject's purpose and background knowledge, and has high subjectivity.

The DIKW model: Information is regarded as the objective processing result of data, with low subjectivity.

Semantic Web and Ontology: The semantics of information are defined through standardized labels, with moderate subjectivity.

Cognitive science: The processing and storage of information depend on an individual's cognitive structure and experiential background, with high subjectivity.

Artificial intelligence: The feature representation of information is usually objective, although training data may have biases and low subjectivity.

9.4 Context dependency

The DIKWP model: The generation and meaning of information depend on the purpose and context of the cognitive subject, with high contextual dependence.

The DIKW model: Information has no context dependency before processing.

Semantic Web and Ontology: The semantics of information depend on its position and association within the ontology, with high context dependency.

Cognitive science: The semantics of information depend on the background and experience of the cognitives, with high contextual dependence.

Artificial intelligence: The feature vectors of information may vary in different application scenarios, with moderate context dependency.

9.5 Dynamicity and constructiveness

The DIKWP model: The process of generating information is dynamic, purpose driven, and has high dynamism and constructiveness.

DIKW model: Information is generated through static processing and organization of data, with moderate dynamism.

Semantic Web and Ontology: Information is constantly updated and expanded through semantic links and ontologies, with high dynamism and constructiveness.

Cognitive science: The storage and processing of information in cognitive structures are dynamic, highly dynamic and constructive.

Artificial intelligence: Information is constantly updated and optimized through feature extraction and model training, with high dynamism and constructiveness.

9.6 Mathematical representation of information

DIKWP model: Represented through purpose driven semantic mapping of information, such as I: X Y.

DIKW model: Without clear mathematical representation, information is the result of data processing.

Semantic Web and Ontology: Representing information entities through ontology and semantic tags.

Cognitive science: Representing information through semantic networks and memory models.

Artificial intelligence: Representing information through feature vectors for computation and inference.

The information definition in Professor Yucong Duan's DIKWP model differs significantly from traditional models, mainly reflected in the following aspects:

Subjectivity and purpose driven: The DIKWP model emphasizes that the generation of information depends on the cognitive subject's purpose and background knowledge, and has a high degree of subjectivity and contextual dependence.

Dynamicity and Constructiveness: The information generation process in the DIKWP model is dynamic and purpose driven, emphasizing the dynamic generation and constructiveness of information semantics.

Semantic processing and association: The information semantics in the DIKWP model are dynamically generated through the cognitive subject's purpose and semantic association, emphasizing the process of purpose driven and dynamic generation.

Context dependency: The information generation and meaning in the DIKWP model are highly dependent on the cognitive subject's context and have high context dependency.

These characteristics enable the DIKWP model to better adapt to complex and ever-changing cognitive needs when processing information, providing a new theoretical framework and practical guidance for fields such as information science, cognitive science, and artificial intelligence.

 

Conclusion

By comparing and analyzing Professor Yucong Duan's DIKWP model with the information definition in traditional information models, we found that the DIKWP model has unique characteristics in information definition, emphasizing the subjectivity, dynamism, and purpose driven semantic processing of information. This model breaks through the objective definition of information in the traditional DIKW model and emphasizes the dynamic generation of information through semantic association and concept confirmation in the cognitive process. The semantic web and ontology provide standardized semantic links, but the DIKWP model focuses more on the dynamic generation and semantic interaction of information within cognitive subjects. Although the definition of information in cognitive science and artificial intelligence overlaps with the DIKWP model in some aspects, the DIKWP model focuses more on the subjectivity and contextual dependence of information.

Overall, the DIKWP model provides a new perspective and theoretical framework for information processing and cognitive science, helping to deepen understanding of the role and significance of information in cognitive processes, and providing new research directions for fields such as natural language processing and artificial intelligence. Future research can further explore the practical application and optimization of the DIKWP model in specific applications, promoting the development and innovation of information processing technology.

 

References

 

[1] Ackoff, R. L. (1989). "From Data to Wisdom". Journal of Applied Systems Analysis.

[2] Rowley, J. (2007). "The wisdom hierarchy: representations of the DIKW hierarchy". Journal of Information Science.

[3] Berners-Lee, T., Hendler, J., & Lassila, O. (2001). "The Semantic Web". Scientific American.

[4] Gruber, T. R. (1993). "A translation approach to portable ontology specifications". Knowledge Acquisition.

[5] Tulving, E. (1972). "Episodic and semantic memory". Organization of Memory.

[6] Collins, A. M., & Quillian, M. R. (9). "Retrieval time from semantic memory". Journal of Verbal Learning and Verbal Behavior.

[7] Brachman, R. J., & Levesque, H. J. (2004). Knowledge Representation and Reasoning.

[8] Russell, S. J., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach.

[9] Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing.

[10] Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing.

[11] Davenport, T. H., & Prusak, L. (1998). Working Knowledge: How Organizations Manage What They Know.

[12] Sowa, J. F. (2000). Knowledge Representation: Logical, Philosophical, and Computational Foundations.

[13] Studer, R., Benjamins, V. R., & Fensel, D. (1998). "Knowledge engineering: Principles and methods". Data & Knowledge Engineering.

[14] Minsky, M. (1974). "A framework for representing knowledge". MIT-AI Laboratory Memo 306.

[15] Newell, A. (1982). "The knowledge level". Artificial Intelligence.

[16] Polanyi, M. (1966). The Tacit Dimension.

[17] Schreiber, G., Akkermans, H., Anjewierden, A., de Hoog, R., Shadbolt, N., Van de Velde, W., & Wielinga, B. (2000). Knowledge Engineering and Management: The CommonKADS Methodology.

[18] Lenat, D. B., & Guha, R. V. (1990). Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project.

[19] Tarski, A. (1944). "The semantic conception of truth and the foundations of semantics". Philosophy and Phenomenological Research.

[20] Chomsky, N. (1957). Syntactic Structures.

[21] Simon, H. A. (1996). The Sciences of the Artificial.

[22] McCarthy, J. (1980). "Circumscriptiona form of non-monotonic reasoning". Artificial Intelligence.

[23] Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.

[24] Russell, S., & Subramanian, D. (1995). "Provably bounded-optimal agents". Journal of Artificial Intelligence Research.

[25] Dean, T., Allen, J., & Aloimonos, Y. (1995). Artificial Intelligence: Theory and Practice.

 

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