The Understanding Theory of Yucong Duan 

Yucong Duan

Benefactor: Shiming Gong

AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory

DIKWP-AC Artificial Consciousness Standardization Committee

World Conference on Artificial Consciousness

World Artificial Consciousness Association

(Email：duanyucong@hotmail.com)

Catalog

Abstract

Introduction

1 Definition of Understanding in the DIKWP Model

1.1 Semantic Association, Probability Confirmation, and Knowledge Reasoning

1.2 Dynamic Generation of Understanding

2 Professor Yucong Duan's Theory of Understanding

3 Comparison with Traditional Cognitive Models' Definition of Understanding

3.1 Understanding in Traditional Cognitive Models

3.2 Comparison Between DIKWP Model and Traditional Models

4 Key Role of Understanding in Information Processing and Knowledge Generation

4.1 Understanding in Information Processing

4.2 Understanding in Knowledge Generation

5 Applications of Understanding in Artificial Intelligence and Cognitive Science

5.1 Understanding in Artificial Intelligence

6 Case Study: Intelligent Medical Diagnosis System

Conclusion

Abstract

Professor Yucong Duan elaborates on the concept of "understanding" in the DIKWP model, emphasizing that understanding is a process of forming new cognitive structures driven by specific purposes through semantic association, probability confirmation, and knowledge reasoning. This paper provides an in-depth analysis of the definition of understanding in the DIKWP model and its applications in semantic processing, knowledge generation, and cognitive processes. By comparing traditional cognitive models, it explores the critical role of understanding in information processing and knowledge generation, particularly focusing on how cognitive subjects interpret semantics when receiving information. It proposes how Professor Duan's theory of understanding can identify and measure misunderstandings through semantic space, ultimately achieving personalized semantic association, supplementation, and cognitive confirmation.

Introduction

In the fields of information science, cognitive science, and artificial intelligence, "understanding" is a key concept. Traditional cognitive models often view understanding as a process of information processing and knowledge generation but tend to overlook the dynamic role of the cognitive subject's purpose and semantic association. Professor Yucong Duan proposes a new definition of understanding in the DIKWP model, emphasizing that understanding is achieved through cognitive subjects' specific purposes, semantic association, probability confirmation, and knowledge reasoning, thereby forming new cognitive structures. This paper will explore this definition in detail and analyze its applications in various fields, particularly how to identify and measure misunderstandings through semantic space, achieving personalized semantic association and cognitive confirmation.

1 Definition of Understanding in the DIKWP Model

In the DIKWP model, understanding is defined as the process by which a cognitive subject forms new cognitive structures through specific purposes, semantic association, probability confirmation, and knowledge reasoning. This process involves multiple levels of cognitive activities, including data processing, information organization, knowledge generation, and the application of wisdom.

1.1 Semantic Association, Probability Confirmation, and Knowledge Reasoning 

The core of understanding lies in semantic association, probability confirmation, and knowledge reasoning:

Semantic Association: Through the cognitive subject's purpose, new information is associated with existing knowledge structures. This process requires identifying the semantic relationships between new information and known concepts.

Probability Confirmation: Through calculation and logical judgment, the probability of the association between new information and existing knowledge structures is confirmed. This process ensures the consistency and rationality of new information within the cognitive subject's knowledge system.

Knowledge Reasoning: Using existing knowledge and reasoning mechanisms, new information is inferred and explained, generating new knowledge and understanding. This includes methods such as deductive reasoning, inductive reasoning, and analogical reasoning.

1.2 Dynamic Generation of Understanding

Understanding is a dynamic process, constantly updating and adjusting as new information is introduced and the cognitive subject's purposes change. This dynamism ensures that the cognitive subject can adapt to changing environments and continuously developing knowledge systems. Misunderstanding Identification and Measurement in Semantic Space Differences Between Cognitive Subject and Receiver Information is often transmitted through natural language, but in the receiver's cognitive space, what is understood is the semantic interpretation of natural language concepts. Due to differences in the concept space and semantic space of the transmitter and receiver, misunderstandings or insufficient understanding may arise during information transmission.

2 Professor Yucong Duan's Theory of Understanding

Professor Yucong Duan's theory of understanding reveals that these misunderstandings and non-understandings can be identified and measured through semantic space. Specifically:

Misunderstanding Identification: By analyzing the receiver's semantic interpretation of natural language concepts, parts that are inconsistent with the transmitter's original intent are identified.

Misunderstanding Measurement: The degree of misunderstanding is quantified by calculating the semantic differences between the receiver and the transmitter.

Personalized Semantic Association: Based on the identification and measurement results, personalized semantic associations are provided, supplementing and modifying information content to better align with the transmitter's original intent.

Knowledge Reasoning: Using knowledge reasoning mechanisms, the semantic associations are further explained and expanded to ensure that the new semantic associations are reasonable and meaningful within the cognitive subject's knowledge system.

Understanding Confirmation: Finally, through adjusted semantic associations and supplements, it is ensured that the information content is correctly understood by the receiver, achieving cognitive confirmation.

3 Comparison with Traditional Cognitive Models' Definition of Understanding

3.1 Understanding in Traditional Cognitive Models

Traditional cognitive models often view understanding as the result of information processing, forming an understanding of new information through information organization and knowledge generation. This process mainly includes the following steps:

Information Processing: Processing input data to extract meaningful information.

Knowledge Generation: Organizing information into knowledge structures to form an understanding of new information.

Application of Wisdom: Verifying and adjusting understanding in practical applications.

3.2 Comparison Between DIKWP Model and Traditional Models

Subjectivity: The DIKWP model emphasizes the subjectivity of understanding, driven by the cognitive subject's purposes through semantic association, probability confirmation, and knowledge reasoning, whereas traditional models typically emphasize the objectivity of information processing.

Dynamism: Understanding in the DIKWP model is a dynamically generated process, constantly updating with new information and the cognitive subject's purposes, while understanding in traditional models is more static and a one-time processing result.

Semantic Association: The DIKWP model emphasizes the key role of semantic association and knowledge reasoning in forming understanding through the identification, confirmation, and reasoning of semantic relationships, while traditional models focus more on information organization and knowledge generation.

Misunderstanding Identification and Measurement: The DIKWP model identifies and measures misunderstandings through semantic space and achieves understanding through personalized semantic association, supplementation, and knowledge reasoning, while traditional models lack mechanisms for misunderstanding identification and dynamic adjustment.

4 Key Role of Understanding in Information Processing and Knowledge Generation

4.1 Understanding in Information Processing

In the information processing process, understanding acts as a bridge, transforming data into meaningful information and knowledge. Driven by the cognitive subject's purposes, semantic association, probability confirmation, and knowledge reasoning are carried out to ensure the consistency and rationality of new information within the knowledge system.

4.2 Understanding in Knowledge Generation

In the knowledge generation process, understanding forms new knowledge structures through dynamic semantic association, probability confirmation, and knowledge reasoning. This process not only involves the expansion of existing knowledge but also the generation and verification of new knowledge.

5 Applications of Understanding in Artificial Intelligence and Cognitive Science

5.1 Understanding in Artificial Intelligence

In AI systems, understanding is primarily achieved through technologies such as semantic analysis, knowledge graphs, and reasoning mechanisms:

Semantic Analysis: AI systems use natural language processing technologies to perform semantic analysis on input natural language, identifying semantic relationships and generating structured information.

Knowledge Graphs: Through knowledge graphs, AI systems semantically associate information from different sources, forming an integrated knowledge network to support complex reasoning and decision-making.

Reasoning Mechanisms: AI systems use reasoning mechanisms (such as deductive reasoning, inductive reasoning, and analogical reasoning) to further explain and expand semantic associations, generating new knowledge and understanding. 5.2 Understanding in Cognitive Science In cognitive science, understanding research mainly focuses on the following aspects:

Cognitive Process Simulation: Simulating human cognitive processes to study the mechanisms of information storage, processing, and retrieval in the brain.

Misunderstanding Identification and Adjustment: Studying how to identify and adjust misunderstandings through cognitive mechanisms to enhance human understanding of information.

Knowledge Reasoning: Studying how humans use existing knowledge to reason and explain, generating new understanding and knowledge structures.

6 Case Study: Intelligent Medical Diagnosis System

In an intelligent medical diagnosis system, doctors use AI technology to assist in diagnosing patient conditions. The system collects various data from patients (such as physical examination data and medical history records) and combines it with the doctor's expertise and medical knowledge base for comprehensive analysis to generate diagnostic information. The following describes the process of generating and processing understanding in this system, demonstrating the application of the DIKWP model's definition of understanding and semantic processing.

Understanding Generation Process

Data Collection and Preliminary Recording: The system collects data from different sources, including physical examination data and medical history records. These data are the basic input of the system and belong to the data level in the DIKWP model.

Semantic Matching and Concept Confirmation: The system semantically matches and confirms concepts with the collected data and the existing medical knowledge base. The system classifies and matches the data semantically, forming preliminary information semantics.

Personalized Semantic Association: According to the doctor's diagnostic purposes, combining data semantics, information semantics, knowledge semantics, and wisdom semantics, personalized diagnostic information is generated.

Probability Confirmation and Knowledge Reasoning: Through probability confirmation and knowledge reasoning, the diagnostic information is further explained and expanded to ensure its rationality and consistency within the doctor's knowledge system.

Understanding Confirmation: Finally, through adjusted semantic associations and supplements, it is ensured that the diagnostic information is correctly understood by the doctor, achieving cognitive confirmation.

Conclusion

By exploring the definition of understanding and semantic processing in Professor Yucong Duan's DIKWP model, we find that understanding is not only the processing and organization of information but also a dynamic, purpose-driven cognitive process. The DIKWP model emphasizes the subjectivity, dynamism, and personalized semantic association of understanding, identifying and measuring misunderstandings to achieve personalized semantic supplementation and adjustment, and generating new understanding and knowledge through knowledge reasoning. This model provides new perspectives and theoretical frameworks for information processing and cognitive science, deepening the understanding of the role and significance of understanding in cognitive processes and offering new research directions for natural language processing, artificial intelligence, and other fields. Future research can further explore the practical applications and optimizations of the DIKWP model, promoting the development and innovation of information processing technologies.