DIKWP as A Bridge Between Human-AI

Yucong Duan

International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation(DIKWP-SC)

World Artificial Consciousness CIC(WAC)

World Conference on Artificial Consciousness(WCAC)

(Email: duanyucong@hotmail.com)

Introduction

The DIKWP model, proposed by Professor Yucong Duan, is an extension of the traditional Data-Information-Knowledge-Wisdom (DIKW) framework, incorporating an additional element called "Purpose." This model aims to mathematically formalize the semantics of the world, providing a cognitively consistent framework for semantic evolution. By eliminating subjective human definitions, the DIKWP model seeks to address misunderstandings and uncertainties in human-machine communication, enabling artificial intelligence systems to process and understand information in a manner consistent with human cognition.

Core Objectives of the DIKWP Model

1. Mathematical Formalization of Semantics: The model provides precise mathematical definitions for Data, Information, Knowledge, Wisdom, and Purpose, aiming to eliminate ambiguities arising from subjective interpretations.

2. Cognitive Consistency: By aligning the mathematical structures with cognitive processes, the model ensures that semantic representations are consistent with human cognition.

3. Semantic Evolution: The networked nature of the DIKWP model allows for dynamic interactions between its components, supporting the evolution of semantics over time.

Framework Overview

The DIKWP model operates within several interconnected spaces:

• Conceptual Space (ConC): Represents the definitions, features, and relationships of concepts as understood by cognitive entities.

• Cognitive Space (ConN): A multidimensional environment where cognitive processing functions transform inputs into outputs.

• Semantic Space (SemA): The network of semantic associations between concepts, formed through experience and knowledge accumulation.

• Consciousness Space: Although not explicitly detailed, it refers to the space where conscious awareness and higher-order cognitive functions occur.

Mathematical Definitions and Representations

1. Data (D)

• Conceptualization: Data are specific manifestations of the same semantics in cognition. They are recognized through shared semantic attributes.

• Mathematical Representation:

  S={f1,f2,...,fn}S = \{f_1, f_2, ..., f_n\}S={f1​,f2​,...,fn​}$$D=\{d\mid d\text{ shares }S\}$$

  Each $d$ is a data element sharing the semantic attribute set $S$.

• Explanation: Data are not just raw observations but are cognitively recognized entities sharing common semantics. This recognition is based on matching semantic features within the cognitive and semantic spaces.

2. Information (I)

• Conceptualization: Information corresponds to one or more "different" semantics, highlighting the differences between new inputs and existing cognitive objects.

• Mathematical Representation:

  $$FI:X\to Y$$

  Where $X$ is a set or combination of DIKWP content semantics (Data, Information, Knowledge, Wisdom, Purpose), and $Y$ is the generated new set or combination of semantics.

• Explanation: Information arises when cognitive entities process differences between new inputs and existing knowledge, generating new semantic associations.

3. Knowledge (K)

• $N$ is the set of concept nodes.

• $E$ is the set of edges representing relationships between concepts.

• $AK$ represents attributes of knowledge.

• Conceptualization: Knowledge represents one or more "complete" semantics formed through abstraction and generalization. It involves understanding relationships and patterns within data and information.

• Mathematical Representation:

  $$K=(N,E,AK)$$

  Where:

• Explanation: Knowledge forms a semantic network where concepts are connected through relationships, allowing for a structured understanding of complex systems.

4. Wisdom (W)

• Conceptualization: Wisdom involves the application of ethics, social morals, and human values to guide decision-making beyond technical considerations.

• Mathematical Representation:

  W:{D,I,K,W,P}→D∗W: \{D, I, K, W, P\} \rightarrow D^*W:{D,I,K,W,P}→D∗

  Where $W$ is a decision function that generates the optimal decision D∗D^*D∗ based on Data, Information, Knowledge, Wisdom, and Purpose.

• Explanation: Wisdom integrates all components of the DIKWP model, emphasizing ethical considerations and human-centric values in cognitive processes.

5. Purpose (P)

• Conceptualization: Purpose denotes goal-oriented processing, defining both the input and desired output semantics.

• Mathematical Representation:

  $$P=(Input,Output)$$$$T:Input\to Output$$

  Where $T$ is a transformation function that maps the input semantics to the desired output semantics.

• Explanation: Purpose provides directionality to cognitive processes, ensuring that all transformations align with predefined goals.

Interrelations and Cognitive Processes

• Networked Relationships: Unlike hierarchical models, the DIKWP components interact in a networked fashion, reflecting the complexity of cognitive processes.

• Semantic Transformations: Each component contributes to semantic evolution through transformations defined by mathematical functions. For example, data is transformed into information through recognition of differences, and information is abstracted into knowledge through identification of complete semantics.

• Cognitive Consistency: By mathematically modeling these transformations, the DIKWP model ensures that semantic evolution within AI systems aligns with human cognitive patterns.

Conceptual and Semantic Spaces

• Conceptual Space (ConC): Contains concepts defined by attributes and relationships, represented mathematically as graphs.

• Semantic Space (SemA): Comprises semantic units (e.g., words, sentences) and their associations, forming a network that represents meaning.

• Cognitive Space (ConN): Encompasses cognitive processing functions that transform inputs into outputs, simulating cognitive activities such as perception, reasoning, and decision-making.

Examples to Illustrate the Model

1. Data Recognition: When observing various apples, a cognitive agent identifies them as "apples" by recognizing shared semantic attributes like shape and texture. This recognition process is mathematically modeled by identifying $S$ and $D$.

2. Information Generation: Noticing that a particular apple is bruised introduces a "difference" from the known concept of "apple." This difference generates new information, processed through $FI$.

3. Knowledge Formation: Generalizing that apples left out in the sun bruise faster involves forming a complete semantic understanding, constructing knowledge represented by $K$.

4. Wisdom Application: Deciding not to leave apples in the sun combines data, information, knowledge, and ethical considerations (e.g., reducing waste), guided by the decision function $W$.

5. Purpose Alignment: The goal of preserving apples defines the Purpose $P$, directing cognitive processes to transform inputs (observations) into outputs (actions to store apples properly).

Supporting Semantic Evolution

• Dynamic Interactions: The networked structure allows for continuous updates and refinements of semantics as new data and information are processed.

• Elimination of Subjectivity: By relying on mathematical definitions, the model reduces ambiguities caused by subjective interpretations, facilitating clearer communication between humans and AI systems.

• Alignment with Human Cognition: The model mirrors human cognitive processes, enabling AI systems to evolve semantically in ways that are consistent with human understanding.

Significance of the DIKWP Model

• Semantic Precision: Provides a rigorous mathematical framework for representing and transforming semantics, crucial for advanced AI systems.

• Cognitive Integration: Integrates various cognitive aspects (data processing, information recognition, knowledge abstraction, wisdom application, and purpose orientation) into a cohesive model.

• Facilitating AI-Human Interaction: Enhances the ability of AI systems to understand and generate human-like semantics, improving interaction and reducing misunderstandings.

• Supporting Ethical AI Development: Emphasizes wisdom and purpose, ensuring that AI systems consider ethical implications and align with human values.

Conclusion

The DIKWP model represents a significant advancement in the semantics-focused mathematization of the world. By providing mathematically precise definitions and networked relationships among Data, Information, Knowledge, Wisdom, and Purpose, it offers a cognitively consistent framework for semantic evolution. This model not only enhances the ability of AI systems to process and understand information in alignment with human cognition but also addresses the challenges of ambiguity and subjectivity in human-machine communication.

By focusing on the mathematical underpinnings of semantics, the DIKWP model paves the way for developing AI systems that are more capable, ethical, and aligned with human cognitive processes. It provides a robust foundation for future research and development in artificial intelligence, cognitive science, and related fields.

Further Exploration

For a deeper understanding of the DIKWP model, one might explore the following areas:

• Graph Theory Applications: Investigate how graph representations of semantic networks facilitate the processing of complex relationships.

• Cognitive Function Modeling: Examine the specific cognitive functions $R$ in the cognitive space and how they simulate human cognitive processes.

• Semantic Space Dynamics: Study the formation and evolution of the semantic space $SemA$ and its role in meaning-making.

• Ethical Frameworks in AI: Analyze how the inclusion of Wisdom and Purpose in the model supports the development of AI systems that adhere to ethical standards.

• Applications in Natural Language Processing: Explore how the DIKWP model can improve language understanding and generation by AI systems.

References

1. International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation (DIKWP-SC),World Association of Artificial Consciousness(WAC),World Conference on Artificial Consciousness(WCAC). Standardization of DIKWP Semantic Mathematics of International Test and Evaluation Standards for Artificial Intelligence based on Networked Data-Information-Knowledge-Wisdom-Purpose (DIKWP ) Model. October 2024 DOI: 10.13140/RG.2.2.26233.89445 .  https://www.researchgate.net/publication/384637381_Standardization_of_DIKWP_Semantic_Mathematics_of_International_Test_and_Evaluation_Standards_for_Artificial_Intelligence_based_on_Networked_Data-Information-Knowledge-Wisdom-Purpose_DIKWP_Model

2. Duan, Y. (2023). The Paradox of Mathematics in AI Semantics. Proposed by Prof. Yucong Duan:" As Prof. Yucong Duan proposed the Paradox of Mathematics as that current mathematics will not reach the goal of supporting real AI development since it goes with the routine of based on abstraction of real semantics but want to reach the reality of semantics. ".