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Scaling Ontologies with Language Models and Math

Scaling Ontologies with Language Models and Math

Slides presented at the ONTOCHAIN Summit for Trustworthy Internet by Marc Green, Software Architect in Cybersecurity

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ONTOCHAIN

June 01, 2022
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  1. Scaling Ontologies with Language Models and Math marcgreen@wpi.edu Marc Green

  2. Problems • Humanity’s corpus of scientific knowledge is not yet

    robustly linked • Ontology engineering is hard, time-consuming, and expensive marcgreen@wpi.edu Marc Green • Ologs provide mathematical robustness to ontologies • Language Models increase efficiency and accessibility of knowledge workflows Solutions
  3. Intro to Ologs (ontology logs) marcgreen@wpi.edu - “Rigorous mathematical framework

    for knowledge representation, construction of scientific models, and data storage” (Spivak, 2011) - More scalable than traditional semantic web standards like RDF Marc Green (Spivak, 2011) (Spivak, 2011)
  4. Intro to Language Models (LMs) - Enable a new paradigm

    of Human-Computer Interaction - B2C for closed domains (chatbots), B2B for open domains (ai assistants) marcgreen@wpi.edu Marc Green https://huggingface.co/blog/large-language-models
  5. Ontochain application idea: Ontology AI Assistant - Public service to

    increase access to and utility of shared understandings - Key Ideas: - Language Models increase accessibility and improve efficiency of working with text - Ologs provide rigorous and scalable mechanism for building shared understandings - Potential features: - Natural Language UI for interacting with ontologies (querying, editing, aligning, etc) - SotA NLP for systematic generation and manipulation of ontologies (eg from existing literature) - Integrates with decentralized network, knowledge graph and knowledge/asset market - Modular architecture allows app to scale as LM tech improves marcgreen@wpi.edu Marc Green
  6. marcgreen@wpi.edu Marc Green

  7. Other References and Related Work • Spivak, 2011: Ologs: a

    categorical framework for knowledge representation (arXiv:1102.1889) • “23% searching …”: https://topos.institute/networked-mathematics • Training and Application of Neural-Network Language Model for Ontology Population (https://www.researchgate.net/publication/347861342_Training_and_Applicati on_of_Neural-Network_Language_Model_for_Ontology_Population) • Ontology-Based Model Abstraction (https://www.researchgate.net/publication/332290797_Ontology-Based_Mode l_Abstraction) • Semantic Scholar by Allen Institute of AI • MathFoldR by Topos Institute marcgreen@wpi.edu Marc Green