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

arXiv: https://arxiv.org/abs/2504.17402 · HTML: https://mlsanity.com/paper/2504.17402/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [Ontology Generation using Large Language Models](https://mlsanity.com/paper/2503.05388/) (2025)
- [LLMs4Life: Large Language Models for Ontology Learning in Life Sciences](https://mlsanity.com/paper/2412.02035/) (2024)
- [The Water Health Open Knowledge Graph](https://mlsanity.com/paper/2305.11051/) (2023)
- [My Ontologist: Evaluating BFO-Based AI for Definition Support](https://mlsanity.com/paper/2407.17657/) (2024)

## Built on by (most cited first, 7 of 7)

- [RSafe: Incentivizing proactive reasoning to build robust and adaptive LLM safeguards](https://mlsanity.com/paper/2506.07736/) (2025)
- [Bench4KE: Benchmarking Automated Competency Question Generation](https://mlsanity.com/paper/2505.24554/) (2025)
- [Large Language Models Assisting Ontology Evaluation](https://mlsanity.com/paper/2507.14552/) (2025)
- [OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs](https://mlsanity.com/paper/2607.17963/) (2026)
- [Constraint-aware Path Planning from Natural Language Instructions Using Large Language Models](https://mlsanity.com/paper/2603.19257/) (2026)
- [When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning](https://mlsanity.com/paper/2608.31118/) (2026)
- [CQ4OE: A benchmark for assessing LLM-assisted ontology generation from competency questions](https://mlsanity.com/paper/2609.26029/) (2026)

## Abstract (arXiv)

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.