Ontology Refinement with Embeddings and LLMs: A Case Study on CASTON

Key points

• Ontologies are widely used to represent and organize complex knowledge in domains such as biomedicine and healthcare.1
• However, constructing reliable ontologies remains challenging, as class hierarchies, whether manually or automatically constructed, may contain subtle subsumption errors that require careful refinement.1
• The embedding model is used to extract a limited number of candidate axioms for refinement together with possible replacement candidates, and an LLM then returns repair suggestions.1
• In a small expert evaluation, the default configuration using a CASTON-fine-tuned OnT embedding model and Qwen3-8B produced reliable high-confidence repairs: all six high-confidence replacements were accepted by both domain experts.1

Sources (1)

• [1]Ontology Refinement with Embeddings and LLMs: A Case Study on CASTON
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 08:10 AM

  We study this problem in CASTON, a casting-domain ontology, and present a lightweight but scalable refinement workflow that combines a fine-tuned ontology embedding model with Large Language Models (LLMs).

  Ontologies are widely used to represent and organize complex knowledge in domains such as biomedicine and healthcare.

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