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arXiv · 2507.14552

Large Language Models Assisting Ontology Evaluation

Anna Sofia Lippolis·Mohammad Javad Saeedizade·Robin Keskisärkkä·Aldo Gangemi·Eva Blomqvist·Andrea Giovanni Nuzzolese
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Abstract

Ontology evaluation through functional requirements, such as testing via competency question (CQ) verification, is a well-established yet costly, labour-intensive, and error-prone endeavour, even for ontology engineering experts. In this work, we introduce OE-Assist, a novel framework designed to assist ontology evaluation through automated and semi-automated CQ verification. By presenting and leveraging a dataset of 1,393 CQs paired with corresponding ontologies and ontology stories, our contributions present, to our knowledge, the first systematic investigation into large language model (LLM)-assisted ontology evaluation, and include: (i) evaluating the effectiveness of a LLM-based approach for automatically performing CQ verification against a manually created gold standard, and (ii) developing and assessing an LLM-powered framework to assist CQ verification with Prot\'eg\'e, by providing suggestions. We found that automated LLM-based evaluation with o1-preview and o3-mini perform at a similar level to the average user's performance.

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Anna Sofia Lippolis, Mohammad Javad Saeedizade, Robin Keskisärkkä, Aldo Gangemi, Eva Blomqvist, Andrea Giovanni Nuzzolese. 2025-07-19. Large Language Models Assisting Ontology Evaluation. https://doi.org/10.1007/978-3-032-09527-5_27

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