Abstract
Ontology extension refers to the process of enriching an existing ontology in response to emerging requirements, making it more complete. This task is resource-intensive and error-prone. Large Language Models (LLMs) have shown promising performance on generating ontologies from scratch, but current approaches rarely tie ontology extension explicitly to requirements or reusable core models, and offer limited, systematic evaluation of LLM outputs.
This paper introduces OntoExtend, a requirements-driven framework for ontology extension with LLMs. It employs retrieval-augmented generation (RAG) over relevant input ontologies and requirements in the form of competency questions to propose grounded extensions.
We evaluate OntoExtend on 39 CQs from two use cases: a public EU-project ontology, Onto-DESIDE, and an industrial ontology from Bosch. The generated fragments show few structural issues, satisfy all functional evaluation tests, and are rated by ontology engineers as requiring minor to moderate revision before integration.
These results suggest that OntoExtend is useful as a drafting assistant for requirement-driven ontology extension in real-world scenarios, while remaining sensitive to CQ specificity and modelling profile.
Blogger's Review: OntoExtend presents an innovative solution for ontology extension by integrating LLMs with a requirement-driven retrieval-augmented generation approach. Its evaluation results in practical applications demonstrate effectiveness, warranting further exploration and application in broader fields.