Expressing Capabilities Among Conceptual Space, Semantic Space, Cognitive Space, and Conscious Space in the DIKWP Framework

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

To enhance understanding of the complex interactions among the Conceptual Space (ConC), Semantic Space (SemA), Cognitive Space (ConN), and Conscious Space, we will explore practical use cases. These cases will illustrate how the DIKWP (Data, Information, Knowledge, Wisdom, Purpose) concepts and semantics manifest in real-world scenarios, highlighting the mutual expressing capabilities among the spaces.

Stakeholders: Physicians, patients, AI developers, and healthcare administrators.

Objective: Develop an AI-powered system that assists physicians in diagnosing diseases by integrating medical data, knowledge, ethical considerations, and patient-specific information.

DIKWP Concepts:

• Data (D): Medical tests results, patient symptoms, vital signs.

• Knowledge (K): Medical ontologies, disease classifications, treatment protocols.

• Concept Definitions:

• "Symptom": Observable indication of a condition.

• "Disease": A disorder or malfunction of the mind or body.

• "Treatment": Medical care given to a patient for an illness or injury.

Representation:

• Knowledge Graph (K): Nodes representing diseases, symptoms, treatments; edges representing relationships (e.g., "has symptom," "treated by").

ConC Table:

DIKWP Semantics:

• Information (I): Meanings derived from patient data.

• Example: Elevated white blood cell count suggests possible infection.

• Contextual Interpretations:

• Symptom Significance: The severity and combination of symptoms may indicate specific diseases.

• Patient History: Previous conditions may influence current diagnosis.

Mappings:

• Semantic Assignment ($\varphi$):

• ϕ("Symptom: Fever")=sFever\phi(\text{"Symptom: Fever"}) = s_{\text{Fever}}ϕ("Symptom: Fever")=sFever​

• ϕ("Symptom: Cough")=sCough\phi(\text{"Symptom: Cough"}) = s_{\text{Cough}}ϕ("Symptom: Cough")=sCough​

SemA Table:

Cognitive Functions:

• Diagnostic Reasoning Function (fDiagnosisf_{\text{Diagnosis}}fDiagnosis​):

• fDiagnosis:S→Kf_{\text{Diagnosis}}: S \rightarrow KfDiagnosis​:S→K

• Processes semantic representations to identify potential diseases.

• Treatment Planning Function (fTreatmentf_{\text{Treatment}}fTreatment​):

• fTreatment:K→If_{\text{Treatment}}: K \rightarrow IfTreatment​:K→I

• Recommends treatments based on diagnosed condition.

Processing:

• Input Semantics: sFever,sCough,sElevated WBC Counts_{\text{Fever}}, s_{\text{Cough}}, s_{\text{Elevated WBC Count}}sFever​,sCough​,sElevated WBC Count​

• Cognitive Processing:

• kPossible Diseases=fDiagnosis(sFever,sCough,sElevated WBC Count)k_{\text{Possible Diseases}} = f_{\text{Diagnosis}}(s_{\text{Fever}}, s_{\text{Cough}}, s_{\text{Elevated WBC Count}})kPossible Diseases​=fDiagnosis​(sFever​,sCough​,sElevated WBC Count​)

• Output Knowledge: Potential diagnosis of "Pneumonia" or "Bronchitis."

ConN Table:

Wisdom and Ethical Considerations:

• Patient-Centered Care: Balancing effective treatment with patient preferences.

• Ethical Guidelines: Do no harm, informed consent, confidentiality.

• Conscious Decision Function ($\Phi$):

• wRecommendation=Φ(fDiagnosis,fTreatment)w_{\text{Recommendation}} = \Phi(f_{\text{Diagnosis}}, f_{\text{Treatment}})wRecommendation​=Φ(fDiagnosis​,fTreatment​)

• Integrates ethical considerations into recommendations.

Purpose Alignment:

• Purpose (P): Provide optimal patient care respecting ethical standards.

• Purpose Alignment Function (TW→PT_{\text{W→P}}TW→P​):

• pAction Plan=TW→P(wRecommendation)p_{\text{Action Plan}} = T_{\text{W→P}}(w_{\text{Recommendation}})pAction Plan​=TW→P​(wRecommendation​)

• Aligns wisdom with actionable steps.

Conscious Space Table:

A. ConC ↔ SemA

• Mapping Concepts to Semantics:

• ϕ("Symptom: Fever")=sFever\phi(\text{"Symptom: Fever"}) = s_{\text{Fever}}ϕ("Symptom: Fever")=sFever​

• Extracting Concepts from Semantics:

• ψ(sFever)="Symptom: Fever"\psi(s_{\text{Fever}}) = \text{"Symptom: Fever"}ψ(sFever​)="Symptom: Fever"

• Distinction:

• Concept (ConC): "Symptom: Fever" as a defined term.

• Semantics (SemA): Meaning of fever in medical diagnosis.

B. SemA ↔ ConN

• Processing Semantics to Generate Knowledge:

• kPossible Diseases=fDiagnosis(sFever,sCough)k_{\text{Possible Diseases}} = f_{\text{Diagnosis}}(s_{\text{Fever}}, s_{\text{Cough}})kPossible Diseases​=fDiagnosis​(sFever​,sCough​)

• Generating New Semantics from Cognitive Processing:

• New understanding of disease patterns.

• Distinction:

• Concepts (ConN): Diagnostic function fDiagnosisf_{\text{Diagnosis}}fDiagnosis​.

• Semantics (SemA): Meanings of how symptoms relate to diseases.

C. ConN ↔ Conscious Space

• Emergence of Wisdom:

• wRecommendation=Φ(fDiagnosis,fTreatment)w_{\text{Recommendation}} = \Phi(f_{\text{Diagnosis}}, f_{\text{Treatment}})wRecommendation​=Φ(fDiagnosis​,fTreatment​)

• Modulating Cognitive Functions with Wisdom:

• Adjusting treatment recommendations based on patient preferences.

• Distinction:

• Concepts (ConN): Cognitive functions performing analysis.

• Semantics (Conscious Space): Ethical implications and patient-centered care.

Ambiguity in Mapping:

• Challenge: Multiple symptoms can indicate various diseases.

• Solution: Utilize probabilistic models to handle uncertainties.

Subjectivity in Conscious Space:

• Challenge: Patient values and ethics vary.

• Solution: Incorporate patient input and ethical guidelines into decision-making.

Tables Summarizing Interactions:

Table A: DIKWP Concepts and Semantics Across Spaces

Table B: Functions and Mappings

Stakeholders: Students, educators, AI developers, and educational institutions.

Objective: Create an AI-driven platform that personalizes learning experiences by adapting to individual student needs, preferences, and learning styles.

DIKWP Concepts:

• Data (D): Student performance data, learning activities, assessment scores.

• Knowledge (K): Curriculum content, educational standards, pedagogical strategies.

• Concept Definitions:

• "Learning Objective": Specific skill or knowledge area to be acquired.

• "Assessment": Tool to measure student understanding.

• "Learning Style": Preferred way a student processes information.

ConC Table:

DIKWP Semantics:

• Information (I): Interpretations of student data.

• Example: "Student struggles with algebraic concepts."

• Contextual Interpretations:

• Learning Preferences: Visual, auditory, kinesthetic modalities.

• Engagement Levels: Indicators of student motivation.

Mappings:

• Semantic Assignment ($\varphi$):

• ϕ("Assessment Score")=sPerformance\phi(\text{"Assessment Score"}) = s_{\text{Performance}}ϕ("Assessment Score")=sPerformance​

• ϕ("Learning Style: Visual")=sVisual Learner\phi(\text{"Learning Style: Visual"}) = s_{\text{Visual Learner}}ϕ("Learning Style: Visual")=sVisual Learner​

SemA Table:

Cognitive Functions:

• Personalization Function (fPersonalizef_{\text{Personalize}}fPersonalize​):

• fPersonalize:S→If_{\text{Personalize}}: S \rightarrow IfPersonalize​:S→I

• Adapts content based on semantics of student data.

• Progress Monitoring Function (fMonitorf_{\text{Monitor}}fMonitor​):

• fMonitor:I→Kf_{\text{Monitor}}: I \rightarrow KfMonitor​:I→K

• Updates knowledge about student progress.

Processing:

• Input Semantics: sPerformance,sVisual Learners_{\text{Performance}}, s_{\text{Visual Learner}}sPerformance​,sVisual Learner​

• Cognitive Processing:

• iAdapted Content=fPersonalize(sPerformance,sVisual Learner)i_{\text{Adapted Content}} = f_{\text{Personalize}}(s_{\text{Performance}}, s_{\text{Visual Learner}})iAdapted Content​=fPersonalize​(sPerformance​,sVisual Learner​)

• Output Information: Customized learning materials.

ConN Table:

Wisdom and Ethical Considerations:

• Equity in Education: Ensuring all students receive appropriate support.

• Privacy and Consent: Respecting student data confidentiality.

• Conscious Decision Function ($\Phi$):

• wEducational Strategy=Φ(fPersonalize,fMonitor)w_{\text{Educational Strategy}} = \Phi(f_{\text{Personalize}}, f_{\text{Monitor}})wEducational Strategy​=Φ(fPersonalize​,fMonitor​)

• Incorporates ethical considerations into educational strategies.

Purpose Alignment:

• Purpose (P): Maximize student learning outcomes while upholding ethical standards.

• Purpose Alignment Function (TW→PT_{\text{W→P}}TW→P​):

• pLearning Plan=TW→P(wEducational Strategy)p_{\text{Learning Plan}} = T_{\text{W→P}}(w_{\text{Educational Strategy}})pLearning Plan​=TW→P​(wEducational Strategy​)

Conscious Space Table:

A. ConC ↔ SemA

• Mapping Concepts to Semantics:

• ϕ("Learning Style: Visual")=sVisual Learner\phi(\text{"Learning Style: Visual"}) = s_{\text{Visual Learner}}ϕ("Learning Style: Visual")=sVisual Learner​

• Extracting Concepts from Semantics:

• ψ(sVisual Learner)="Learning Style: Visual"\psi(s_{\text{Visual Learner}}) = \text{"Learning Style: Visual"}ψ(sVisual Learner​)="Learning Style: Visual"

• Distinction:

• Concept (ConC): "Learning Style: Visual" as a defined attribute.

• Semantics (SemA): Understanding how a visual learner prefers to process information.

B. SemA ↔ ConN

• Processing Semantics to Adapt Content:

• iAdapted Content=fPersonalize(sVisual Learner)i_{\text{Adapted Content}} = f_{\text{Personalize}}(s_{\text{Visual Learner}})iAdapted Content​=fPersonalize​(sVisual Learner​)

• Generating New Semantics from Cognitive Processing:

• New insights into effective teaching strategies.

• Distinction:

• Concepts (ConN): Personalization function fPersonalizef_{\text{Personalize}}fPersonalize​.

• Semantics (SemA): Meanings of how learning styles influence content adaptation.

C. ConN ↔ Conscious Space

• Emergence of Wisdom:

• wEducational Strategy=Φ(fPersonalize,fMonitor)w_{\text{Educational Strategy}} = \Phi(f_{\text{Personalize}}, f_{\text{Monitor}})wEducational Strategy​=Φ(fPersonalize​,fMonitor​)

• Modulating Cognitive Functions with Wisdom:

• Adjusting personalization to ensure fairness and privacy.

• Distinction:

• Concepts (ConN): Cognitive functions managing personalization.

• Semantics (Conscious Space): Ethical considerations in educational practices.

Privacy Concerns:

• Challenge: Handling sensitive student data ethically.

• Solution: Implement data anonymization and obtain informed consent.

Equity in Personalization:

• Challenge: Ensuring that personalization does not reinforce biases.

• Solution: Regularly audit algorithms for fairness and adjust as needed.

Tables Summarizing Interactions:

Table A: DIKWP Concepts and Semantics Across Spaces

Table B: Functions and Mappings

1. Mutual Expression Enhances Functionality:

• Concepts and semantics from different spaces mutually inform and enhance each other, leading to more effective systems.

2. DIKWP Framework Provides Structure:

• The DIKWP model offers a clear framework for understanding and modeling the interactions among spaces.

3. Ethical Considerations are Integral:

• Wisdom and purpose, emerging in the Conscious Space, are essential for guiding actions in ethically responsible ways.

4. Challenges Require Thoughtful Solutions:

• Limitations such as ambiguity, subjectivity, and privacy concerns must be addressed through careful design and ethical practices.

Conclusion

These use cases demonstrate how the mutual expressing capabilities among the Conceptual Space, Semantic Space, Cognitive Space, and Conscious Space can be modeled using the DIKWP framework, with explicit distinctions between DIKWP concepts and semantics. By applying these concepts to practical scenarios, we gain a deeper understanding of:

• How data and concepts are given meaning and context.

• The way cognitive functions process and transform these meanings into knowledge and wisdom.

• The emergence of ethical considerations and purposeful actions in complex systems.

This approach facilitates the design of AI systems that are not only effective but also ethically responsible and aligned with human values.

Further Study

• Explore Additional Use Cases: Investigate other domains such as finance, environmental management, or social media to see how the DIKWP framework applies.

• Develop Interactive Models: Create simulations or prototypes to observe these concepts in action.

• Engage in Ethical Discussions: Consider the broader implications of integrating wisdom and purpose into AI systems.

References for Further Reading

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. ".