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Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI).

Sabrina Toro1, Anna V Anagnostopoulos2, Susan M Bello2

  • 1University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

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|October 16, 2024
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Summary
This summary is machine-generated.

Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI) aids ontology creation by generating components from diverse sources. While effective, expert oversight remains crucial for refining AI-generated definitions and relationships.

Keywords:
Artificial intelligenceBiocurationKnowledge graphsLarge language modelsOntologiesOntology engineering

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Area of Science:

  • Informatics
  • Biomedical Science
  • Environmental Science
  • Food Science

Background:

  • Ontologies are crucial for informatics infrastructure, representing knowledge in computable formats.
  • Constructing and maintaining ontologies requires significant resources and expert collaboration.
  • DRAGON-AI is a novel method for ontology generation using AI.

Purpose of the Study:

  • To introduce and evaluate DRAGON-AI, an AI-driven method for generating ontology components.
  • To assess the performance of DRAGON-AI in de novo term construction across diverse ontologies.
  • To explore the integration of natural language instructions into the ontology generation process.

Main Methods:

  • Utilized Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) for ontology component generation.
  • Generated textual and logical ontology components from existing ontologies and unstructured text.
  • Incorporated natural language instructions via GitHub issues.

Main Results:

  • DRAGON-AI demonstrated high precision in relationship generation.
  • AI-generated definitions were acceptable but scored lower than human-authored ones.
  • Expert evaluators with high domain confidence better identified flaws in AI outputs.

Conclusions:

  • DRAGON-AI shows potential to significantly assist manual ontology construction.
  • Expert curators and editors are essential to guide AI-driven ontology development.
  • The method can incorporate user feedback through natural language instructions.