How good are Large Language Models at Ontology Engineering?

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Invited talk at SEMANTICS 2026. Abstract: Large Language Models (LLMs) have revolutionized research across many domains, including knowledge engineering. However, ontology development remains a complex and labor-intensive task that requires substantial domain expertise to ensure adequate competency question coverage, adherence to modeling best practices, effective vocabulary reuse, and high-quality documentation. In this talk, I will present an overview of our recent work on using LLMs and agentic AI to support ontology engineering, with a particular focus on the end-to-end generation of ontologies from a set of competency questions. We explore how LLM-based agents can leverage external tools to iteratively design, validate, refine, and document ontologies rather than simply generating them in a single pass. The talk will discuss the practical challenges of evaluating ontology quality, challenges of generating reliable benchmarks, as well as the importance of explainability, transparency, and traceability in the resulting ontologies. Overall, the talk will reflect on both the opportunities and limitations of LLMs in ontology engineering, and ask a broader question: should LLMs merely assist ontology engineers, or can they reliably take on a more autonomous role in the ontology engineering process?