LLMs turbocharge ontology development
LLMs turbocharge ontology development
Ontological Knowledge Bases (OKBs) organize domain know-how, but hand-crafting them is slow and brittle. This research shows how Large Language Models can help experts build and refine OKBs faster—and with more consistency.
The team proposes a structured, iterative workflow that uses LLMs to:
- extract and normalize domain concepts
- generate ontology artifacts (classes, relations, constraints)
- run continuous refinement cycles with human oversight
In a vehicle sales case study (a "user context profile" ontology), the method delivered quicker construction, improved coherence, better bias checks, and clearer traceability of design choices. The result: OKBs that scale, integrate with other systems, and evolve as the domain changes.
Why it matters: Smarter, transparent OKBs power search, recommendations, and analytics across industries.
By Le Ngoc Luyen, Marie-Hélène Abel, and Philippe Gouspillou. Read: https://arxiv.org/abs/2601.10436v1
Paper: https://arxiv.org/abs/2601.10436v1
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#AI #LLM #Ontology #KnowledgeGraph #SemanticWeb #KnowledgeManagement #InformationRetrieval
