MLKN.model

Unified Theories of Cognition

Inspired by ACT-R, SOAR, and Global Workspace Theory, MLKN.model treats knowledge as a cognitive architecture, where disciplines and concepts interact like modules in a unified system.

Network Science

Uses graph theory to model knowledge as a network of nodes (concepts) and edges (relationships), enabling analysis of centrality, modularity, and diffusion.

Systems Theory

Applies systems thinking to knowledge, treating disciplines as interconnected components of a larger, adaptive system.

Computational Epistemology

Formalizes knowledge as a computational system, where the structure of knowledge can be analyzed, simulated, and optimized.

Layer 1: Knowledge Representation

Polyhierarchical Graph Structure:

• Nodes: Core Domains, Disciplines, Subdisciplines, Thematic Domains, Concepts (Layers 1-5).
• Edges: Hierarchical (\( \mathcal{T} \)), Interdisciplinary, and Thematic connections (\( \mathcal{S} \)).
• Metadata: Layer, domain, weight, type, and descriptions.

Data Sources: OpenAlex, Scopus, MeSH, IEEE Thesaurus.

Formal Definition: See \( \mathcal{H} = (V, \mathcal{S}, \mathcal{P}, \mathcal{T}, \omega, \tau) \) in the Mathematical Foundations.

Layer 2: Network Analysis

Graph-Theoretic Algorithms:

• Centrality Metrics: Degree, betweenness, eigenvector centrality.
• Community Detection: Modularity, Louvain, Leiden algorithms.
• Path Analysis: Shortest paths, random walks, diffusion processes.
• Topological Analysis: Persistent homology (see TDA).

Tools: NetworkX (Python), Vis.js (JavaScript), D3.js.

Layer 3: Dynamic Simulation

Predictive and Generative Models:

• Knowledge Evolution: Simulate the emergence of new disciplines using \( \mathbf{T}_{\mathcal{H}} \).
• Hypothesis Testing: Test meta-science hypotheses (e.g., "Does interdisciplinarity increase citation impact?").
• AI Integration: Use knowledge graphs to enhance AI reasoning (e.g., RAG, semantic search).
• Adaptive Learning: Personalize knowledge exploration for users.

Tools: TensorFlow, PyTorch, Scikit-learn.

Formal Definition: See \( \mathbf{T}_{\mathcal{H}} \) in the Mathematical Foundations.

Layer 1: Core Domains

6 broad categories (e.g., Natural Sciences, Social Sciences).

Layer 2: Disciplines

25 fields (e.g., Physics, Psychology).

Layer 3: Subdisciplines

235 specialized areas (e.g., Quantum Mechanics, Cognitive Psychology).

Layer 4: Thematic Domains

Granular clusters (e.g., Machine Learning, Attention).

Layer 5: Concepts

Specific topics (e.g., Neural Networks, Memory).

Metascience

Test hypotheses about the structure and evolution of science:

• How do new disciplines emerge?
• What are the most influential fields?
• How does knowledge diffuse across domains?

AI and Machine Learning

Enhance AI reasoning and knowledge representation:

• Retrieval-Augmented Generation (RAG): Ground LLMs in structured knowledge.
• Semantic Search: Improve search with knowledge graphs.
• Explainable AI: Use knowledge networks to interpret AI decisions.

Education

Transform learning and teaching:

• Adaptive Learning: Personalize education with knowledge maps.
• Interdisciplinary Curricula: Design courses that bridge disciplines.
• Knowledge Assessment: Measure understanding of complex systems.

Policy and Decision-Making

Inform science policy and innovation:

• Research Funding: Identify gaps and opportunities in science.
• Collaboration Networks: Optimize team composition for interdisciplinary projects.
• Innovation Forecasting: Predict emerging fields and technologies.

Real-Time Knowledge Mapping

Continuously update the knowledge network with new publications and data.

Predictive Modeling

Develop machine learning models to predict the evolution of disciplines.

AI Integration

Integrate with large language models (LLMs) for reasoning and generation.

Collaborative Platform

Build a global community for knowledge mapping and meta-science.

Core Technologies

• Python: NetworkX, Pandas, NumPy, SciPy.
• JavaScript: D3.js, Vis.js, Cytoscape.js.
• Data: OpenAlex, Scopus, MeSH, IEEE Thesaurus.

Performance

• Nodes: 31,590.
• Edges: 320,082.
• Layers: 5 (Core Domains → Concepts).
• Scalability: Optimized for large-scale networks.

Open Science

• License: MIT License.
• Data: Publicly available (JSON/CSV).
• Code: Open-source on GitHub.