The DIKWP Model Simplified

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)

The DIKWP model, proposed by Prof. Yucong Duan, extends the traditional Data-Information-Knowledge-Wisdom (DIKW) hierarchy by adding Purpose as a fifth element. This model offers a comprehensive framework for understanding how cognitive entities (such as humans or AI systems) process and conceptualize information at various levels of abstraction and complexity. Below is an overview of each component in the DIKWP model:

Data in the DIKWP model is not just raw facts or observations. Instead, data is viewed as specific manifestations of shared semantics within the cognitive space of entities. When cognitive processes handle data, they seek and extract shared semantics that label these data concepts, unifying them under the same concept based on these shared features.

• Key Points:

• Shared Semantics: Data concepts share common semantic attributes, allowing them to be grouped together despite superficial differences.

• Cognitive Processing: The recognition of data involves matching new observations with existing concepts in the cognitive entity's semantic space.

• Mathematical Representation:

  S={f1,f2,…,fn}D={d∣d shares S}\begin{align*} S & = \{ f_1, f_2, \dots, f_n \} \\ D & = \{ d \mid d \text{ shares } S \} \end{align*}SD​={f1​,f2​,…,fn​}={d∣d shares S}​

  Where $S$ is the set of shared semantic attributes fif_ifi​, and $D$ is the set of data instances $d$ that share these attributes.

Information corresponds to recognizing differences in semantics within the cognitive space. It involves a Purpose-driven process where cognitive entities identify and classify differences between new inputs and existing cognitive objects, generating new semantic associations.

• Key Points:

• Differences in Semantics: Information arises from identifying variations or new patterns that differ from existing knowledge.

• Purpose-Driven Processing: The cognitive entity uses its goals or purposes to interpret and integrate new information.

• Mathematical Representation:

  FI:X→YF_I : X \rightarrow YFI​:X→Y

  Where FIF_IFI​ is the information processing function, $X$ represents input semantics (combinations of DIKWP content), and $Y$ represents the output of new semantic associations.

Knowledge represents the abstraction and generalization of entities, events, and laws, corresponding to "complete" semantics in the cognitive space. It is formed through higher-order cognitive activities that assign completeness to observations, often using assumptions to generalize from partial data.

• Key Points:

• Abstraction and Generalization: Knowledge involves creating broader concepts or rules from specific instances.

• Semantic Networks: Knowledge is structured, forming interconnected concepts and relationships.

• Mathematical Representation:

  $$K=(N,E)$$

  Each edge ese_ses​ represents a relationship $r$ between concepts nin_ini​ and njn_jnj​:

  es=(ni,nj,r)e_s = (n_i, n_j, r)es​=(ni​,nj​,r)

• N={n1,n2,…,nk}N = \{ n_1, n_2, \dots, n_k \}N={n1​,n2​,…,nk​}: Set of concept nodes.

• E={e1,e2,…,em}E = \{ e_1, e_2, \dots, e_m \}E={e1​,e2​,…,em​}: Set of relationships between concepts.

Wisdom involves integrating ethics, social morals, human nature, and similar aspects into the decision-making process. It represents a higher level of understanding derived from cultural and societal norms, guiding cognitive entities to consider ethical and moral factors beyond technical efficiency.

• Key Points:

• Ethical Considerations: Wisdom requires balancing various factors like ethics, feasibility, and social impact.

• Value Systems: It is rooted in core human values and serves as a foundation for decision-making.

• 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 the wisdom function that takes Data ($D$), Information ($I$), Knowledge ($K$), existing Wisdom ($W$), and Purpose ($P$) to produce the optimal decision D∗D^*D∗.

Purpose provides the goal-oriented aspect of cognitive processes. It represents stakeholders' understanding of a phenomenon or problem (Input) and the objectives they aim to achieve (Output). Purpose guides the transformation of input semantics into desired output semantics.

• Key Points:

• Goal-Oriented Processing: Cognitive activities are driven by specific goals or purposes.

• Transformation Functions: Purpose involves mapping inputs to outputs to achieve desired objectives.

• Mathematical Representation:

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

  Where $T$ is the transformation function guided by Purpose.

The DIKWP model emphasizes the dynamic and interconnected nature of Data, Information, Knowledge, Wisdom, and Purpose in cognitive processes:

• Data is recognized through shared semantics, forming the foundation of cognitive recognition.

• Information arises from identifying differences and generating new semantic associations.

• Knowledge is constructed by abstracting and generalizing complete semantics, forming structured networks.

• Wisdom integrates ethical and moral considerations, guiding decision-making beyond mere data and information.

• Purpose drives the entire cognitive process, setting goals and directing the transformation of inputs into desired outputs.

• Cognitive Science: The model provides insights into how humans perceive, process, and utilize information at different abstraction levels.

• Artificial Intelligence: It offers a framework for designing AI systems that can handle data processing, knowledge representation, ethical decision-making, and goal-oriented behaviors.

• Knowledge Management: Organizations can apply the DIKWP model to enhance information processing, decision-making, and strategic planning.

• Subjectivity and Objectivity: The model acknowledges the subjective nature of data interpretation and the influence of existing cognitive structures.

• Ethics and Morality: By incorporating Wisdom, the model emphasizes the importance of ethical considerations in cognition and decision-making.

• Teleology (Purpose): The inclusion of Purpose aligns with philosophical views that actions are goal-directed and that cognition is driven by objectives.

By integrating Purpose into the traditional DIKW hierarchy, Prof. Duan's DIKWP model offers a more holistic understanding of cognitive processes. It highlights the importance of goal orientation and ethical considerations in the interpretation and utilization of information, providing a valuable framework for both theoretical exploration and practical application in various fields.

References for Further Exploration

• 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

• 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. ".