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)

Building upon our previous investigations into the Data-Information-Knowledge-Wisdom-Purpose (DIKWP) Semantic Mathematics framework proposed by Prof. Yucong Duan, this document presents a detailed exposition of the new version of DIKWP Semantic Mathematics. This enhanced framework addresses the limitations and paradoxes discussed earlier, such as Gödel's Incompleteness Theorems, Russell's Paradox, and the cognitive limitations of human understanding. By refining the foundational semantics and introducing new mechanisms, the updated DIKWP Semantic Mathematics aims to provide a more robust and comprehensive approach to modeling natural language semantics and human cognition.

The original DIKWP Semantic Mathematics framework is based on the manipulation of three fundamental semantics:

• Sameness (Data)

• Difference (Information)

• Completeness (Knowledge)

This framework aims to model all natural language semantics by iteratively applying these semantics. However, our previous investigations have highlighted several challenges and limitations:

• Gödel's Incompleteness Theorems: The framework may be subject to the limitations of formal systems.

• Russell's Paradox: Potential for paradoxical constructs in set formation.

• Cognitive Limitations: Human cognitive space may have inherent limits affecting the understanding and proof of complex problems.

• Paradox of the DIKWP Cognitive Semantic Space: If all explanations exist within the cognitive semantic space, but some remain inaccessible, this raises questions about the completeness and accessibility of knowledge.

In response to these challenges, the new version of DIKWP Semantic Mathematics introduces enhancements to address these issues while maintaining the core principles of the original framework.

• Incompleteness: To accommodate Gödel's incompleteness, the new framework incorporates mechanisms to recognize and handle undecidable propositions.

• Paradoxes: Introducing safeguards against self-referential paradoxes like Russell's Paradox.

• Cognitive Limits: Acknowledging human cognitive limitations and integrating methods to extend cognitive capabilities through the framework.

• Accessibility of Knowledge: Improving mechanisms for discovering and accessing explanations within the cognitive semantic space.

• Introducing Additional Semantics: Augmenting the original three semantics with new elements to capture more complex relationships.

• Dynamic Semantics: Allowing for the evolution and adaptation of semantics over time.

The new version retains the original components but introduces enhancements:

1. Data (Sameness): Recognition of shared attributes among entities.

2. Information (Difference): Identification of distinctions between entities.

3. Knowledge (Completeness): Integration of attributes to form holistic concepts.

4. Wisdom (Contextualization): Applying knowledge appropriately in context.

5. Purpose (Intentionality): Guiding actions and reasoning based on goals.

To address the limitations, two new semantics are introduced:

1. Possibility (Potentiality): Captures potential states or explanations that are not yet realized or discovered.

2. Limitation (Constraint): Recognizes inherent constraints within systems or cognition.

• Definition: A set of potential attributes or relationships that may exist but are not currently confirmed.

• Representation: P={p1,p2,...,pn}P = \{ p_1, p_2, ..., p_n \}P={p1​,p2​,...,pn​}

• Role: Allows the framework to account for undecidable propositions and unknown explanations.

• Definition: A set of constraints or boundaries that limit the applicability or understanding of certain semantics.

• Representation: L={l1,l2,...,lm}L = \{ l_1, l_2, ..., l_m \}L={l1​,l2​,...,lm​}

• Role: Acknowledges the inherent limitations of formal systems and human cognition.

• Purpose: Introduces a higher-level semantic layer to analyze and manage the interactions between fundamental semantics.

• Functionality: Enables reflection on the semantics themselves, allowing the system to recognize limitations and adjust accordingly.

• Purpose: Incorporates type theory to prevent self-referential paradoxes.

• Implementation: Assigns types to semantics, restricting operations that could lead to contradictions.

• Purpose: Organizes semantics into hierarchical levels to manage complexity and prevent circular definitions.

• Implementation: Establishes clear relationships between higher-level and lower-level semantics.

• Recognition of Undecidable Propositions: The Possibility Semantics $P$ allows the framework to represent statements that may be true but unprovable within the system.

• Meta-Semantic Analysis: The Meta-Semantic Layer enables the system to recognize its own limitations and the existence of undecidable propositions.

• Type Restrictions: By assigning types to sets and semantics, self-referential definitions that lead to paradoxes are avoided.

• Hierarchical Structuring: Separates levels of abstraction to prevent circular references.

• Acknowledging Limitations: The Limitation Semantics $L$ explicitly represents cognitive and systemic constraints.

• Extending Cognition: The framework provides tools to navigate around cognitive limitations by structuring knowledge in a way that maximizes accessibility.

• Discovery Mechanisms: Enhanced search algorithms within the framework assist in uncovering explanations or proofs that exist within the cognitive semantic space.

• Adaptive Learning: The framework can evolve by incorporating new semantics as they emerge, ensuring that the cognitive semantic space remains current.

Traditional Approach:

• Sameness: Recognizing that even numbers share the property of being divisible by 2.

• Difference: Distinguishing between prime and composite numbers.

• Completeness: Attempting to integrate these properties to prove the conjecture.

Enhanced Approach:

• Possibility Semantics $P$: Acknowledges that a proof may exist in the realm of potential explanations not yet discovered.

• Limitation Semantics $L$: Recognizes the current limitations in number theory that prevent a proof.

• Meta-Semantic Analysis: The framework reflects on the limitations and directs efforts towards areas with higher potential for breakthroughs.

Traditional Approach:

• Set Formation: Creating sets based on the Difference semantics.

• Paradox Emergence: Self-referential sets lead to contradictions.

Enhanced Approach:

• Type Theory Integration: Assigns types to sets to prevent them from containing themselves.

• Hierarchical Structuring: Ensures that sets of a certain type can only contain elements of a lower type.

• Paradox Prevention: Incorporating type theory and hierarchical structuring minimizes the risk of paradoxes.

• Handling Incompleteness: The framework can acknowledge and work with undecidable propositions.

• Accessibility of Knowledge: Improved mechanisms for discovering explanations within the cognitive semantic space.

• Adaptive Learning: Ability to evolve with new knowledge and semantics.

• Expanded Semantics: The addition of Possibility and Limitation semantics allows for a more nuanced representation of knowledge.

• Meta-Cognition: The Meta-Semantic Layer enables the system to reflect on its own processes and limitations.

• Resolution: By introducing the Limitation Semantics $L$ and acknowledging that not all explanations may be accessible, the paradox is addressed.

• Implication: The framework accepts that while explanations may exist within the cognitive semantic space, practical limitations may prevent their discovery.

• Extension of Cognitive Capabilities: The framework provides tools to navigate cognitive limitations, but also recognizes boundaries through Limitation Semantics $L$.

• Collaboration with AI: The framework can be implemented in AI systems to augment human cognition.

The new version of the DIKWP Semantic Mathematics framework enhances the original model by introducing additional fundamental semantics and mechanisms to address previously identified limitations and paradoxes. By incorporating Possibility and Limitation semantics, integrating type theory, and establishing a Meta-Semantic Layer, the framework becomes more robust and capable of modeling complex semantic structures while acknowledging inherent constraints.

This enhanced framework offers:

• Improved handling of undecidable propositions and paradoxes.

• A more comprehensive cognitive semantic space that adapts to new knowledge.

• Tools to extend and support human cognition within recognized limitations.

1. 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 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. ".

2. Gödel, K. (1931). On Formally Undecidable Propositions of Principia Mathematica and Related Systems. Monatshefte für Mathematik und Physik.

3. Russell, B. (1908). Mathematical Logic as Based on the Theory of Types. American Journal of Mathematics, 30(3), 222-262.

4. Sowa, J. F. (2000). Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks/Cole.

5. Chalmers, D. J. (1990). Why Fodor and Pylyshyn Were Wrong: The Simplest Refutation. Department of Philosophy, Indiana University.

I extend sincere gratitude to Prof. Yucong Duan for his pioneering work on the DIKWP Semantic Mathematics framework and for inspiring this comprehensive update that addresses key challenges and advances the field of semantic modeling.

For further discussion on the enhanced DIKWP Semantic Mathematics framework, please contact [Author's Name] at [Contact Information].

Keywords: DIKWP Model, Semantic Mathematics, Enhanced Framework, Sameness, Difference, Completeness, Possibility Semantics, Limitation Semantics, Prof. Yucong Duan, Cognitive Semantic Space, Artificial Intelligence

In this detailed exposition, we have introduced the new version of the DIKWP Semantic Mathematics framework, building upon our previous investigations and addressing the identified limitations and paradoxes. The enhanced framework maintains the core principles of the original model while incorporating additional semantics and mechanisms to provide a more robust and comprehensive approach to modeling natural language semantics and human cognition.