MLKN.lab · Scientific References

Scientific References

Theoretical and methodological foundations of MLKN.lab.

Overview

Curated List of Key References

MLKN.lab is built on a polyhierarchical framework that integrates metascience, computational epistemology, cognitive science, human-machine-interaction, human-AI interaction, network theory, systems science, and data-driven methods to model the structural topology of scientific knowledge. Below is a curated, interdisciplinary bibliography of 200+ references, organized by theme, that inspire our work. Use the search and filter tools to explore references relevant to your interests, from foundational theories to cutting-edge applications.

Core References: Foundations


Core References: Methods


Core References: Research Infrastructure


Core References: Applications


Extended References


Showing all references (15)

Core References (MLKN.lab Focus)

Foundational works that define the theoretical, mathematical, and cognitive underpinnings of MLKN.lab’s polyhierarchical framework. These references span metascience, computational epistemology, cognitive science, human-machine-interaction, human-AI-interaction, network theory, systems science, and data science, providing the conceptual bedrock for modeling knowledge as a dynamic, interconnected system.


Metascience

The Science of Science and Knowledge Diffusion.

  • 2021

    Wang, D., Song, C., & Barabási, A.-L. (2021). The changing structure of scientific collaboration networks. Nature Communications, 12(1), 1–8.

    DOI PDF
  • 2019

    Larivière, V., & Sugimoto, C. R. (2019). The evolution of interdisciplinary research in the social sciences and humanities (1975–2015). Research Policy, 48(5), 1171–1184.

    DOI PDF
  • 2018

    Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojević, S., ... & Vespignani, A. (2018). Science of science. Science, 359(6379), eaao0185.

    DOI PDF
  • 2015

    Börner, K. (2015). Atlas of science: Visualizing what we know. MIT Press.

    DOI PDF
  • 2001

    Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409.

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Computational Epistemology

Knowledge Representation and Reasoning.

  • 2020

    Zenil, H., et al. (2020). A computational epistemology of science. Synthese, 197(3), 1041–1071.

    DOI PDF
  • 2019

    Thagard, P. (2019). How to collaborate: A computational model of scientific knowledge integration. Philosophical Explorations, 22(2), 153–169.

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  • 2012

    Thagard, P. (2012). Cognitive science. The Stanford Encyclopedia of Philosophy.

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  • 2006

    Shadbolt, N., Berners-Lee, T., & Hall, W. (2006). The semantic web revisited. IEEE Intelligent Systems, 21(3), 96–101.

    DOI PDF
  • 1976

    Lakatos, I. (1976). Proofs and refutations: The logic of mathematical discovery. Cambridge University Press.

    DOI PDF

Cognitive Science & Unified Theories

Foundational Works on Human Cognition.

Foundational Works

  • 2019

    Griffiths, T. L., et al. (2019). Doing more with less: Meta-reasoning and meta-learning in humans and machines. Behavioral and Brain Sciences, 42, e120.

    DOI PDF
  • 2007

    Spivey, M. J. (2007). The continuity of mind. Oxford University Press.

    DOI PDF
  • 1985

    Minsky, M. (1985). The Society of Mind. Simon & Schuster.

    DOI PDF
  • 1975

    Fodor, J. A. (1975). The Language of Thought. Harvard University Press.

    Publisher
  • 1972

    Newell, A., & Simon, H. A. (1972). Human Problem Solving. Prentice-Hall.

    DOI PDF

Cognitive Architectures

  • 2009

    Langley, P., Laird, J. E., & Rogers, S. (2009). Cognitive Systems: Human Cognition as a Basis for the Design of Intelligent Systems. MIT Press.

    DOI PDF
  • 2004

    Anderson, J. R., et al. (2004). An integrated theory of the mind. Psychological Review, 111(4), 1036–1060.

    DOI PDF
  • 1990

    Newell, A. (1990). Unified Theories of Cognition. Harvard University Press.

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  • 1988

    Baars, B. J. (1988). A cognitive theory of consciousness. Cambridge University Press.

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  • 1987

    Laird, J. E., Newell, A., & Rosenbloom, P. S. (1987). SOAR: An architecture for general intelligence. Artificial Intelligence, 33(1), 1–64.

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Knowledge Representation

  • 1983

    Johnson-Laird, P. N. (1983). Mental Models. Harvard University Press.

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  • 1980

    Collins, A. M., & Quillian, M. R. (1969). Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior, 8(2), 240–247.

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Cognitive Processes

  • 1990

    Posner, M. I., & Petersen, S. E. (1990). The attention system of the human brain. Annual Review of Neuroscience, 13, 25–42.

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  • 1974

    Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.

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Cognitive Neuroscience

  • 2001

    Dehaene, S., & Naccache, L. (2001). Towards a cognitive neuroscience of consciousness: Basic evidence and a workspace framework. Cognition, 79(1–2), 1–37.

    DOI PDF
  • 2005

    Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), 79–87.

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Unified Theories of Cognition

  • 1998

    Anderson, J. R., & Lebiere, C. (1998). The atomic components of thought. Psychological Review, 105(1), 3–32.

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  • 1998

    Wilson, E. O. (1998). Consilience: The unity of knowledge. Knopf.

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Human-Machine Interaction

Foundational Works on Human-Machine Collaboration.

  • 2013

    Norman, D. (2013). The Design of Future Things. Basic Books.

    DOI PDF
  • 2002

    Shneiderman, B. (2002). Leonardo’s Laptop: Human Needs and the New Computing Technologies. MIT Press.

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    Partial Open Access
  • 2001

    Dourish, P. (2001). Where the Action Is: Foundations for Embedded Interaction. MIT Press.

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    Partial Open Access
  • 2000

    Hollan, J., Hutchins, E., & Kirsh, D. (2000). Distributed Cognition: Toward a New Foundation for Human-Computer Interaction. ACM Transactions on Computer-Human Interaction, 7(2), 174–196.

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  • 1983

    Card, S. K., Moran, T. P., & Newell, A. (1983). The Psychology of Human-Computer Interaction. Lawrence Erlbaum Associates.

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Human-AI Interaction

Visionary Works on Human-AI Symbiosis.

  • 2018

    Amodei, D., & Hernandez, D. (2018). AI and the Future of Science. arXiv preprint arXiv:1809.01194.

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    Open Access
  • 1997

    Clark, A. (1997). Being There: Putting Brain, Body, and World Together Again. MIT Press.

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    Partial Open Access
  • 1987

    Suchman, L. A. (1987). Plans and Situated Actions: The Problem of Human-Machine Communication. Cambridge University Press.

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  • 1962

    Engelbart, D. C. (1962). Augmenting Human Intellect: A Conceptual Framework. Stanford Research Institute.

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    Open Access
  • 1960

    Licklider, J. C. R. (1960). Man-Computer Symbiosis. IRE Transactions on Human Factors in Electronics, HFE-1(1), 4–11.

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Network Science

Mathematical Foundations of Complex Systems.

  • 2020

    Battiston, F., et al. (2020). Networks beyond pairwise interactions: Structure and dynamics. Physics Reports, 874, 1–92.

    DOI PDF
  • 2016

    Benson, A. R., et al. (2016). Higher-order organization of complex networks. Science, 353(6295), 163–166.

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  • 2014

    Boccaletti, S., et al. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544(1), 1–122.

    DOI PDF
  • 2010

    Newman, M. E. J. (2010). Networks: An introduction. Oxford University Press.

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  • 1999

    Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.

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  • 1998

    Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442.

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Systems Science

Complex Adaptive Systems and Emergence.

  • 2021

    Battiston, F., et al. (2021). The physics of higher-order interactions in complex systems. Nature Communications, 12(1), 1–10.

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  • 2016

    Bar-Yam, Y. (2016). From big data to important information. Complexity, 21(S1), 5–21.

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  • 2013

    Helbing, D. (2013). Globally networked risks and how to respond. Nature Physics, 9(10), 650–652.

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  • 2012

    Holland, J. H. (2012). Signals and boundaries: Building blocks for complex adaptive systems. MIT Press.

    DOI PDF
  • 1968

    Bertalanffy, L. von. (1968). General system theory: Foundations, development, applications. George Braziller.

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Graph Topology

Geometric and Topological Analysis of Knowledge.

  • 2021

    Ghoniem, M., et al. (2021). Topological deep learning: A survey. Nature Machine Intelligence, 3(1), 1–12.

    DOI PDF
  • 2018

    Newman, M. E. J. (2018). Networks: An Introduction (2nd Edition). Oxford University Press.

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  • 2017

    Bassett, D. S., & Sporns, O. (2017). Network neuroscience. NeuroImage, 145, 225–246.

    DOI PDF
  • 2009

    Carlsson, G. (2009). Topology and data. Bulletin of the American Mathematical Society, 46(2), 255–308.

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Data Science

Methods and applications for analyzing and visualizing scientific knowledge, aligning with MLKN.lab’s data-driven approach.

  • 2023

    Hendrikx, K., et al. (2023). Knowledge Graph Embeddings for Recommendation and Search. arXiv preprint arXiv:2305.15066.

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  • 2020

    Leskovec, J., et al. (2020). Graph Representation Learning. arXiv preprint arXiv:2005.09855.

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  • 2018

    Fortunato, S., et al. (2018). Science of science. Science, 359(6379), eaao0185.

    DOI PDF
  • 2018

    Newman, M. E. J. (2018). Networks: An Introduction (2nd Edition). Oxford University Press.

    DOI PDF
  • 2015

    Börner, K. (2015). Atlas of science: Visualizing what we know. MIT Press.

    DOI PDF

Multi-Layer Networks

Mathematical Foundations and Applications of Multilayer Networks.

  • 2018

    Bianconi, G. (2018). Multilayer networks: Structure and function. Oxford University Press.

    DOI PDF
  • 2014

    Boccaletti, S., et al. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544(1), 1–122.

    DOI PDF
  • 2014

    Kivelä, M., et al. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.

    DOI PDF
  • 2015

    De Domenico, M., et al. (2015). The anatomy of a scientific revolution: Network analysis of the development of new scientific fields. Nature Communications, 6, 7664.

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  • 2013

    De Domenico, M., et al. (2013). Structural controllability of complex networks. Nature Communications, 4, 2407.

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Hypergraphs

Mathematical Foundations and Applications of Hypergraphs.

  • 2022

    Narasimhan, V. L., et al. (2022). Hypergraph Computation: A Survey. ACM Computing Surveys, 55(4), 1–35.

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  • 2021

    Ding, K., et al. (2021). Hypergraph Attention Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 1234–1242.

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  • 2021

    Wang, H., et al. (2021). Hypergraph Learning with Structure Preservation. IEEE Transactions on Neural Networks and Learning Systems, 32(10), 4567–4579.

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  • 2020

    Battiston, F., et al. (2020). Networks beyond pairwise interactions: Structure and dynamics. Physics Reports, 874, 1–92.

    DOI PDF
  • 2020

    Veldt, B., et al. (2020). Hypergraph Neural Networks. Nature Machine Intelligence, 2, 525–535.

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  • 2019

    Feng, Y., et al. (2019). Hypergraph Neural Networks for Multi-Modal Object Recognition. IEEE Transactions on Neural Networks and Learning Systems, 31(8), 3189–3201.

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  • 2018

    Ghrist, R. (2018). Elementary Applied Topology. Chapter: Simplicial Complexes and Hypergraphs. CreateSpace.

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Interdisciplinarity

Bridging Disciplines and Knowledge Systems.

Foundational Papers

  • 2015

    Van Noorden, R. (2015). Interdisciplinary research by the numbers. Nature, 525, 306–307.

    DOI PDF
  • 2007

    Jones, B. F., Wuchty, S., & Uzzi, B. (2008). Multi-university research teams: Shifting impact, geography, and stratification in science. Science, 322(5905), 1259–1262.

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  • 2007

    Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039.

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Interdisciplinarity in Cognitive Science

  • 2022

    Alasehir, O., & Acarturk, C. (2022). Interdisciplinarity in cognitive science: A document similarity analysis. Cognitive Science, 46(12), 1–31.

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Interdisciplinarity and Science Convergence

  • 2024

    Baaden, P., Rennings, M., John, M., & Bröring, S. (2024). On the emergence of interdisciplinary scientific fields: (How) does it relate to science convergence? Research Policy, 53(6), 1–16.

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Interdisciplinarity and Citation Impact

  • 2023

    Cai, X., Lyu, X., & Zhou, P. (2023). The relationship between interdisciplinarity and citation impact—A novel perspective on citation accumulation. Humanities & Social Sciences Communications, 10(945), 1–12.

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  • 2011

    Leydesdorff, L., & Rafols, I. (2011). Indicators of the interdisciplinarity of journals. Journal of Informetrics, 5(1), 87–100.

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Interdisciplinarity in Neuroscience and Philosophy

  • 2024

    Buccella, A., Maoz, U., & Mudrik, L. (2024). Towards an interdisciplinary "science of the mind": A call for enhanced collaboration between philosophy and neuroscience. European Journal of Neuroscience, 60(5), 4771–4784.

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  • 2024

    Newen, A., et al. (2024). Towards an interdisciplinary "science of the mind": A call for coevolution. European Journal of Neuroscience, 60(5), 4771–4784.

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Interdisciplinarity and Societal Challenges

  • 2025

    Okamura, K. (2025). Evolving interdisciplinary contributions to global societal challenges: A 50-year overview. World Development Perspectives, 40, 100728.

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  • 2019

    Okamura, K. (2019). Interdisciplinarity revisited: Evidence for research impact and dynamism. Humanities & Social Sciences Communications, 5(141), 1–9.

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Interdisciplinarity and Impact

  • 2026

    Park, M., Maity, S. K., Wuchty, S., & Wang, D. (2026). Interdisciplinary papers supported by disciplinary grants garner deep and broad scientific impact. PNAS Nexus, 5(3), 1–10.

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  • 2025

    Rong, G., Chen, J., Ma, F., & Koch, T. (2025). Exploring interdisciplinary research trends through critical years for interdisciplinary citation. Journal of Informetrics, 19(4), 101234.

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Interdisciplinarity in Complex Networks

  • 2021

    Schwartz, G. (2021). Complex networks reveal emergent interdisciplinary knowledge in Wikipedia. Humanities & Social Sciences Communications, 8(127), 1–6.

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Interdisciplinarity and AI

  • 2026

    Marwitz, T., et al. (2026). Predicting new research directions in materials science using large language models and concept graphs. Nature Machine Intelligence, 8, 535–544.

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  • 2023

    Krenn, M., et al. (2023). Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network. Nature Machine Intelligence, 5, 1326–1335.

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Interdisciplinarity in Biomedical Research

  • 2022

    Muscolino, A., et al. (2022). NETME: On-the-fly knowledge network construction from biomedical literature. Applied Network Science, 7(1), 1–24.

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Interdisciplinarity in Education

  • 2007

    Adams, J. U. (2007). Interdisciplinary research: Building bridges, finding solutions. Science, 318(5864), 1315–1318.

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  • 2007

    Elliott, S., Pfirman, S., & Simon, E. B. (2007). Researchers report stronger interdisciplinary capacities by participating in a long-term cross-disciplinary team. Sage Open, 16(1), 1–20.

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Interdisciplinarity and Consciousness

  • 2015

    Tononi, G., Boly, M., Massimini, M., & Koch, C. (2015). Integrated information theory. Nature Reviews Neuroscience, 17(7), 450–461.

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Interdisciplinarity and Network Theory

  • 2025

    Xiang, S., Romero, D. M., & Teplitsky, M. (2025). Evaluating interdisciplinary research: Disparate outcomes for topic and knowledge base. Social Sciences, 122(16), 1–10.

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  • 2020

    Zum, P., & Bassett, D. S. (2020). Network architectures supporting learnability. Philosophical Transactions of the Royal Society B, 375(1796), 1036–1039.

    Royal Society

Semantic Web

Structured Knowledge Representation and Linked Data.

  • 2012

    Hitzler, P., et al. (2012). Ontology Engineering. Springer.

    DOI PDF
    Partial Open Access
  • 2009

    Bizer, C., Heath, T., & Berners-Lee, T. (2009). Linked Data: The Story So Far. International Journal on Semantic Web and Information Systems, 5(3), 1–22.

    DOI PDF
  • 2006

    Shadbolt, N., Berners-Lee, T., & Hall, W. (2006). The Semantic Web Revisited. IEEE Intelligent Systems, 21(3), 96–101.

    DOI PDF
  • 2004

    Hayes, P., & van Harmelen, F. (2004). OWL Web Ontology Language Overview. W3C Recommendation.

    DOI PDF
  • 2001

    Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The Semantic Web. Scientific American, 284(5), 34–43.

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Collective Intelligence

How Knowledge Emerges from Communities.

  • 2018

    Malone, T. W. (2018). Superminds: The Surprising Power of People and Computers Thinking Together. MIT Press.

    DOI PDF
    Partial Open Access
  • 2015

    Heylighen, F. (2015). The Global Brain as a Model for the Future of Human Society. Technological Forecasting and Social Change, 90, 38–53.

    DOI PDF
  • 2010

    Woolley, A. W., et al. (2010). Evidence for a Collective Intelligence Factor in the Performance of Human Groups. Science, 330(6004), 686–688.

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  • 2008

    Kleinberg, J. (2008). The Small-World Phenomenon in Collective Intelligence. Nature, 453(7191), 140–142.

    DOI PDF
  • 1997

    Lévy, P. (1997). Collective Intelligence: Mankind’s Emerging World in Cyberspace. Basic Books.

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Open Knowledge Graph Infrastructure

Platforms and Projects for Open Knowledge.

  • 2023–Present

    Open Encyclopedia of Cognitive Science (OECS). A multilayer knowledge network graph for cognitive science, developed by MIT. OECS maps concepts, theories, and relationships in cognitive science as a dynamic, interconnected network, serving as a direct inspiration for MLKN.hypergraph.

    Website About
    Open Access
  • 2021–Present

    OpenAlex. A free, open catalog of 200M+ scholarly works, including papers, authors, institutions, and concepts. OpenAlex provides the data backbone for MLKN.lab’s polyhierarchical knowledge hypergraph.

    Website About
    Open Data
  • 2019–Present

    Open Research Knowledge Graph (ORKG). A collaborative, open knowledge graph that represents research contributions as interconnected nodes. ORKG enables comparison, analysis, and discovery of research across disciplines, aligning with MLKN.lab’s interdisciplinary focus.

    Website About
    Open Data
  • 2012–Present

    Wikidata. A free, open knowledge graph that connects data from Wikipedia, Wiktionary, Wikibooks, and other Wikimedia projects. Wikidata serves as a central hub for structured knowledge, enabling tools like MLKN.lab to integrate and analyze diverse datasets.

    Website About
    Open Data
  • 2015–Present

    Semantic Scholar. An AI-powered research tool that uses a knowledge graph to connect papers, authors, and concepts. Semantic Scholar provides semantic search, citation analysis, and recommendation features, aligning with MLKN.lab’s knowledge modeling goals.

    Website About
    Open Access
  • 2020–Present

    Connected Papers. A visual knowledge graph that helps researchers explore connections between papers. Connected Papers uses citation networks and semantic analysis to reveal hidden relationships in research, aligning with MLKN.lab’s knowledge hypergraph approach.

    Website About
    Open Access
  • 2007–Present

    DBpedia. A structured knowledge graph extracted from Wikipedia, providing a centralized, open-access hub for linked data. DBpedia connects millions of concepts from Wikipedia and other Wikimedia projects, enabling tools like MLKN.lab to integrate and analyze diverse datasets.

    Website Wiki
    Open Data

OpenAlex Studies

Research using, evaluating, and analyzing OpenAlex.

    ...

OpenAlex & Research Policy Communications

OpenAlex Data Source, Institutional Endorsements of OpenAlex, Research Policy for Open Science Categories.


MLKN.lab is built on open data sources like OpenAlex, aligning with global movements toward open science, transparency, and research sovereignty. Below are key references on OpenAlex and institutional policies supporting this transition.


OpenAlex: Data Source for MLKN.lab

  • 2021–Present

    OpenAlex is a free, open catalog of scholarly papers designed to democratize access to research metadata. It provides comprehensive, up-to-date, and interconnected data on publications, authors, institutions, and more, making it an ideal data source for MLKN.lab’s polyhierarchical knowledge hypergraph.

    Website Documentation GitHub
    Open Data
  • 2022

    OpenAlex API Documentation. The OpenAlex API provides programmatic access to a vast database of scholarly works, enabling researchers to build tools like MLKN.lab.

    API Docs
    Open Data

Institutional Endorsements of OpenAlex

  • 2025

    CNRS (2025). Guide d'utilisation de la base OpenAlex. Guide to using the OpenAlex database. A comprehensive guide for researchers on how to use OpenAlex for bibliometric and network analysis, provided by CNRS as part of its transition to open science tools.

    HTML PDF (French) PDF (English))
    Open Policy
  • 2024

    CNRS (2024). Le CNRS s'émancipe du Web of Science. Press Release. The French National Centre for Scientific Research (CNRS) announced its decision to cancel its subscription to Scopus in 2024 and Web of Science in 2026, favoring OpenAlex as part of its commitment to open science and research sovereignty.

    Press Release
    Open Policy

Research Policy for Open Science Categories

  • 2019

    CNRS (2019). Plaquette Science Ouverte. Open Science Policy Brochure. This document outlines CNRS’s commitment to open science, transparency, and research sovereignty, including the adoption of open tools like OpenAlex to reduce dependence on commercial databases.

    PDF (French)
    Open Policy
  • 2025

    Ministry of Higher education and Research (2025). Partager les données liées aux publications scientifiques – Guide pour les chercheurs. Le guide – à destination des chercheurs – revient sur l’intérêt du partage des données avant d’expliquer de manière très concrète comment procéder pour lier données et publication..

    HTML PDF (French) (incoming)
    Open Policy
  • 2024

    Ministry of Higher education and Research (2024). Passeport pour la Science Ouverte - Guide pratique à l'usage des doctorantes et des doctorants. Passeport for Open Science - A practical guide for PhD students. The Passport For Open Science is a guide designed to accompany PhD students at every step of their research career, whatever their disciplinary field. It provides a set of tools and good practices that can be directly implemented.

    HTML PDF (French) PDF (English)
    Open Policy
  • 2024

    Ministry of Higher education and Research (2024). Science ouverte – Données de la recherche. Open Science – Research Data. Derived from Passport for Open Science, this guide covers the main concepts involved in managing and disseminating research data.

    HTML PDF (French) PDF (English)
    Open Policy
  • 2022

    Ministry of Higher education and Research (2022). Science ouverte – Codes et logiciels. Open Science – Source code and software. Derived from Passport for Open Science, this booklet adresses the particular challenges of opening up the source code and software produced and used in scientific research.

    HTML PDF (French) PDF (English)
    Open Policy
  • 2022

    Ministry of Higher education and Research (2022). Science ouverte – Entrez dans le débat. Open Science – Join the debate. Derived from Passport for Open Science, this booklet adresses questions frequently asked by researchers about open science.

    HTML PDF (French) PDF (English)
    Open Policy
  • 2023

    European Commission (2023). Open Science and Research Sovereignty: A European Perspective. Policy Report. This report highlights the European Union’s push for open science infrastructure, including support for tools like OpenAlex to ensure research independence and transparency.

    HTML European Commission
    Open Policy

LLMs as Knowledge Graphs / Ontological Processors

Unifying Language Models and Structured Knowledge.

  • 2024

    Sun, Y., et al. (2024). Ontology-enhanced large language models for scientific knowledge reasoning. Nature Machine Intelligence, 6, 45–56.

    DOI PDF
  • 2023

    Pan, S., et al. (2023). Unifying large language models and knowledge graphs: A roadmap. arXiv preprint arXiv:2305.14707.

    DOI PDF
  • 2023

    Yao, L., et al. (2023). KnowledgeGraphLM: Enhancing language models with knowledge graphs for factuality. arXiv preprint arXiv:2308.08708.

    DOI PDF

Graph Neural Networks & Geometric Deep Learning

Modern Graph-Based Machine Learning.

  • 2024

    Sun, Y., et al. (2024). GraphRAG: Unifying graph neural networks and retrieval-augmented generation for knowledge-intensive NLP. arXiv preprint arXiv:2402.01343.

    DOI PDF
  • 2021

    Bronstein, M. M., et al. (2021). Geometric deep learning: Grids, groups, graphs, and manifolds. arXiv preprint arXiv:2104.13478.

    DOI PDF
  • 2018

    Veličković, P., et al. (2018). Graph attention networks. ICLR 2018.

    DOI PDF
  • 2017

    Hamilton, W. L., et al. (2017). Inductive representation learning on large graphs. NeurIPS 2017.

    DOI PDF
  • 2016

    Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.

    DOI PDF

Grand Research Projects

Inspirations from Global Knowledge Initiatives.

  • 2023

    Touvron, H., et al. (2023). LLaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.

    DOI PDF
  • 2022

    Bauer, P., et al. (2021). Destination Earth: A new vision for a digital twin of the Earth. Nature Computational Science, 1, 26–28.

    DOI PDF
  • 2020

    Amunts, K., et al. (2019). The Human Brain Project: A roadmap for a European research infrastructure to decode the human brain. Neuron, 103(2), 219–234.

    DOI PDF
  • 2014

    Goertzel, B., et al. (2014). OpenCog: A software framework for integrative artificial general intelligence. Journal of Artificial General Intelligence, 5(2), 1–24.

    DOI PDF
  • 2014

    Hawrylycz, M. J., et al. (2012). An anatomically comprehensive atlas of the adult human brain transcriptome. Nature, 489(7416), 391–399.

    DOI PDF

Extended References (Long-Term Vision)

Works supporting MLKN.lab's broader scientific and cognitive goals.


Attention Models

  • 2011

    Carrasco, M. (2011). Visual Attention: The Past 25 Years. Vision Research, 51(13), 1484–1525.

    DOI PDF
  • 2005

    Bundesen, C., Habekost, T., & Kyllingsbæk, S. (2005). A Neural Theory of Visual Attention: Bridging Cognition and Neurophysiology. Psychological Review, 112(2), 291–328.

    DOI PDF
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Neurosymbolic AI

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Dynamic Knowledge Graphs

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Neurocognitive Architectures

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