Assessing the Capability of Large Language Models for Domain-Specific Ontology Generation

• URL: http://arxiv.org/abs/2504.17402v1
• Date: Thu, 24 Apr 2025 09:47:14 GMT
• Title: Assessing the Capability of Large Language Models for Domain-Specific Ontology Generation
• Authors: Anna Sofia Lippolis, Mohammad Javad Saeedizade, Robin Keskisarkka, Aldo Gangemi, Eva Blomqvist, Andrea Giovanni Nuzzolese,
• Abstract summary: Large Language Models (LLMs) have shown significant potential for ontology engineering.<n>We investigate the generalizability of two state-of-the-art LLMs, DeepSeek and o1-preview, by generating from a set of competency questions.<n>Our findings show that the performance of the experiments is remarkably consistent across all domains.
• Score: 1.099532646524593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have shown significant potential for ontology engineering. However, it is still unclear to what extent they are applicable to the task of domain-specific ontology generation. In this study, we explore the application of LLMs for automated ontology generation and evaluate their performance across different domains. Specifically, we investigate the generalizability of two state-of-the-art LLMs, DeepSeek and o1-preview, both equipped with reasoning capabilities, by generating ontologies from a set of competency questions (CQs) and related user stories. Our experimental setup comprises six distinct domains carried out in existing ontology engineering projects and a total of 95 curated CQs designed to test the models' reasoning for ontology engineering. Our findings show that with both LLMs, the performance of the experiments is remarkably consistent across all domains, indicating that these methods are capable of generalizing ontology generation tasks irrespective of the domain. These results highlight the potential of LLM-based approaches in achieving scalable and domain-agnostic ontology construction and lay the groundwork for further research into enhancing automated reasoning and knowledge representation techniques.

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