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Figure 1: Interactive MLKN.hypergraph mapping knowledge topology across 25 disciplines and 5 ontological layers.
Click the image to launch the live interactive map.
Table of Contents
MLKN.lab (Multi-Layered Knowledge Network Ideas Laboratory) is a computational metascience platform that maps the structural topology of human knowledge through a polyhierarchical, data-driven framework. It models scientific knowledge as a dynamic, multi-layered hypergraph, revealing hidden structures, bridges, and gaps across disciplines.
Traditional flat classifications (e.g., OpenAlex’s 4 domains) miss critical interdisciplinary connections. Our 6-core-domain polyhierarchy captures emergent structures in science, enabling:
- Discovery of hidden knowledge bridges (e.g., how "Transformers" diffused from AI to Neuroscience).
- Validation of metascience hypotheses (e.g., ARI benchmarking).
- Augmentation of human and AI reasoning (e.g., interactive knowledge maps).
Built from bibliometric analysis of large-scale scholarly metadata (OpenAlex), MLKN.lab operationalizes a polyhierarchy across:
- 5 Ontological Layers (Core Domains → Concepts).
- 6 Epistemological Core Domains (Formal Sciences, Natural Sciences, Engineering & Technology, Life Sciences, Health & Medical Sciences, Social Sciences & Humanities).
- 25 Academic Disciplines (e.g., Computer Science, Neuroscience).
- 235 Subdisciplines (e.g., AI, Cognitive Psychology).
- 320K+ Interdisciplinary Connections.
MLKN.lab serves as:
- Research Platform: A living laboratory for testing hypotheses about the evolution of science.
- Scientific Instrument: A high-precision tool for graph-theoretic analysis of knowledge networks.
- Cognitive Interface: A generative system to augment human reasoning through interactive knowledge maps.
Core Paradigms & Domains
metascience·science of science·computational epistemology·network science·systems science·interdisciplinary research·cognitive science·human-machine interaction·human-ai interaction·semantic web·collective intelligence·unified theory of knowledge
Network Science & Complex Systems:
network science·complex systems·graph topology·multilayer networks·hypergraphs·knowledge network·polyhierarchy·knowledge graph
Methods & Dynamics:
research mapping·knowledge diffusion·data science·data visualization·scientometrics·bibliometrics
Ontology & Knowledge Organization:
ontology·taxonomy·knowledge organization systems·SKOS·Frascati Classification·OpenAlex
Interaction & Open Science Foundations:
human-machine interaction·human-ai interaction·open science·open source·open data·open education·FAIR principles
- 326,862 rows (CSV)
- 31,590 nodes (JSON)
- 320,075 edges (JSON)
- 5 Ontological Layers
- 6 Core Domains
- 25 Academic Disciplines
- 235 Subdisciplines
- 5 Ontological Layers: Core Domains → Academic Disciplines → Subdisciplines → Core Thematic Domains → Main Concepts.
- 6 Core Domains: Formal Sciences, Natural Sciences, Engineering & Technology, Life Sciences, Health & Medical Sciences, Social Sciences & Humanities.
- Reclassified Ontology: Extends OpenAlex’s 4-domain taxonomy to 6 domains for balanced representation and interdisciplinarity.
- Interdisciplinary Knowledge Network: Visualize all 25 disciplines simultaneously, organized into 6 Core Domains.
- 25 Disciplinary Knowledge Networks: Explore individual discipline networks (e.g., Computer Science, Neuroscience, Psychology).
- Interactive Visualizations: Powered by D3.js and NetworkX for dynamic exploration.
- Data Source: OpenAlex (March 2026 snapshot).
- Classification Logic: Aligns with OECD Frascati Manual and UNESCO Fields of Science.
- Validation: Network metrics (centrality, modularity) and expert review ensure accuracy.
- For Researchers: Uncover hidden connections, identify emerging fields, and test meta-science hypotheses.
- For Educators: Design interdisciplinary curricula and adaptive learning paths.
- For Policymakers: Inform science policy by visualizing gaps, silos, and opportunities.
- For AI Developers: Enhance AI reasoning with structured knowledge networks.
| Feature | MLKN.lab | Traditional Approaches |
|-------------------------|-----------------------------------|----------------------------------|
| **Hierarchy** | 5 ontological layers | Flat or 2–3 layers |
| **Interdisciplinarity** | 6 balanced core domains | Biased toward STEM |
| **Validation** | ARI benchmarking + expert review | Manual classification only |
| **Visualization** | Interactive D3.js hypergraphs | Static or limited to 2D networks |
MLKN-lab/
│
├── index.html # Homepage
│
├── research/ # Research
│ ├── research.html # Research focus
│ ├── model.html # MLKN.model
│ ├── model-mathematical-foundations.html # MLKN.model - Mathematical Foundations
│ └── method.html # Method
│
├── knowledge_network/ # Knowledge network visualizations
│ ├── polyhierarchy.html # Polyhierarchy
│ ├── MLKN-hypergraph.html # Interactive hypergraph
│ └── data.html # Data
│
├── knowledge_library/ # Bibliography and monographs
│ ├── references.html # Scientific references
│ └── open-access-monographs.html # Open-access monographs
│ └── normative-references.html # Normative references
│
├── applications/ # Applications
│ ├── applications.html # General applications
│ └── essay.html # Essays & explorations
│ └── normative-applications.html # Normative applications
│
├── about/ # About
│ ├── about.html # About MLKN.lab
│ └── inspirations.html # Inspirations
│ └── normative-foundations.html # Normative foundations
│
├── data/ # Datasets
│ ├── MLKN_Hierarchy_Master_File_All_layers_All_Details_Final.csv # Master hierarchy (92.9 MB)
│ ├── MLKN_hypergraph_nodes.json # Nodes for visualization (5.6 MB)
│ └── MLKN_hypergraph_edges.json # Edges for visualization (40.5 MB)
│
├── tutorials/ # Jupyter notebooks
│ └── MLKN_tutorial.ipynb # Quickstart guide [](https://colab.research.google.com/github/FrancoisPapin/MLKN-lab/blob/main/tutorials/MLKN_tutorial.ipynb)
│
├── css/ # Stylesheets
│ └── style.css # Main styles
│
├── citation/ # Citation
│ └── CITATION.cff # Cite as
│
└── README.md # This file
- Python 3.x (for data analysis)
- Jupyter Notebook (for tutorials)
- D3.js (for interactive visualizations)
- Clone the repository:
git clone https://github.com/FrancoisPapin/MLKN-lab.git cd MLKN-lab - Install dependencies:
pip install pandas networkx matplotlib
- Load the master hierarchy CSV into a DataFrame:
import pandas as pd df = pd.read_csv("data/MLKN_Hierarchy_Master_File_All_layers_All_Details_Final.csv") print(df.head())
- Visualize the hypergraph using the provided JSON files:
import json with open("data/MLKN_hypergraph_nodes.json") as f: nodes = json.load(f) with open("data/MLKN_hypergraph_edges.json") as f: edges = json.load(f)
- Explore the MLKN dataset interactively in your browser without local installation:
| **File** | **Format** | **Size** | **Purpose** |
|---------------------------------------------------------------|------------|------------|---------------------------------------|
| `MLKN_Hierarchy_Master_File_All_layers_All_Details_Final.csv` | CSV | 92.9 MB | Master hierarchy for analysis |
| `MLKN_hypergraph_nodes.json` | JSON | 5.6 MB | Nodes for interactive visualization |
| `MLKN_hypergraph_edges.json` | JSON | 40.5 MB | Edges for interactive visualization |
| `MLKN_tutorial.ipynb` | Jupyter | 534 Bytes | Quickstart guide |
- Analyze interdisciplinarity, knowledge diffusion, or the evolution of scientific fields.
- Example: Track how concepts like "Transformers" diffuse from AI to neuroscience.
- Model how knowledge is structured, validated, and evolved in computational systems.
- Develop AI systems that reason over scientific knowledge.
- Study the topological structure of scientific knowledge using NetworkX or Gephi.
- Identify key disciplines, bridges between fields, or emerging research areas.
- Explore the MLKN.hypergraph.
- Build custom network explorers for specific research questions.
MLKN.lab is grounded in meta-science, computational epistemology, network theory, and cognitive science. Explore 100+ scientific references that foundation MLKN.lab, including:
- Fortunato, S., et al. (2018). Science of Science. Science.
- Börner, K. (2015). Atlas of science: Visualizing what we know. MIT Press.
- Thagard, P. (2019). How to Collaborate: A Computational Model of Scientific Knowledge Integration. Philosophical Explorations.
- Zenil, H., et al. (2020). A computational epistemology of science. Synthese.
- Barabási, A.-L., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science.
- Boccaletti, S., et al. (2014). The Structure and Dynamics of Multilayer Networks. Physics Reports.
- Anderson, J. R., & Lebiere, C. (1998). The atomic components of thought. Psychological Review.
- Langley, P., Laird, J. E., & Rogers, S. (2009). Cognitive Systems: Human Cognition as a Basis for the Design of Intelligent Systems. MIT Press.
Explore 200+ open-access monographs that inspire MLKN.lab, including:
- Seibt, J., Hakli, R., & Nørskov, M. (Eds.). (2026). Robophilosophy: Philosophy of, for, and by Social Robotics. MIT Press.
- Chirimuuta, M. (Ed.). (2024). The Brain Abstracted: Simplification in the History and Philosophy of Neuroscience. MIT Press.
- Nersessian, N. J. (Ed.). (2022). Interdisciplinarity in the Making: Models and Methods in Frontier Science. MIT Press.
🔗 Full Open-Access Monographs Collection
We welcome contributions! Here’s how you can help:
- Report Issues: Open an issue on GitHub for bugs or feature requests.
- Suggest Improvements: Propose enhancements to the hierarchy or visualizations.
- Cite MLKN.lab: If you use our website, please cite:
Papin, F. (2026). MLKN.lab: A Polyhierarchical Framework for Modeling the Scientific Knowledge [Software]. https://francoispapin.github.io/MLKN-lab/ - Cite MLKN.lab Dataset: If you use our data or tools, please cite:
Papin, F. (2026). MLKN.lab: A Polyhierarchical Hypergraph of Scientific Knowledge for Metascience, Computational Epistemology, and Network Analysis [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.21363227
The MLKN.lab project is an open-science initiative. We welcome contributions, feature requests, and domain ontology refinements!
- Repository: https://github.com/francoispapin/MLKN-lab/
- Interactive Visualization: https://github.com/francoispapin/MLKN-lab/
- Get Involved: Open an issue or discussion on GitHub to collaborate on graph extensions, new mappings, or downstream applications.
- Author: François Papin
- Website: https://francoispapin.github.io/MLKN-lab/
- GitHub: https://github.com/FrancoisPapin/MLKN-lab
This project is licensed under the MIT License – see the LICENSE file for details.
- MLKN.labV1 (Previous experimental version)
- Zenodo Dataset (DOI: 10.5281/zenodo.21363227)
- OpenAlex (Data source)
- OECD Frascati Manual (Classification standard)