LLMs turbocharge ontology development

Kari Jaaskelainen

16 Jan 2026 — 1 min read

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

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