大模型可跨领域自动生成本体，且表现稳定可靠。
Assessing the Capability of Large Language Models for Domain-Specific Ontology Generation
- 用竞品问题和用户故事驱动大模型生成本体
- 在6个领域中95个问题上表现一致，跨域泛化能力强
- 适合需要快速构建通用本体的开发者与研究者
大型语言模型（LLMs）在本体工程中展现出巨大潜力，但其在特定领域本体生成任务中的适用性仍不明确。本研究探索了基于LLM的自动化本体生成方法，并在多个领域评估其性能。具体而言，我们利用具备推理能力的两个前沿模型DeepSeek和o1-preview，通过一组95个经过筛选的竞品问题（CQs）和相关用户故事，生成本体。实验涵盖六个已有本体工程项目的不同领域。结果表明，两种模型在所有领域中表现高度一致，说明其具备跨领域泛化能力。这凸显了基于大模型的方法在实现可扩展、领域无关的本体构建方面的潜力，并为提升自动化推理与知识表示技术奠定了基础。
原文摘要 · Abstract (English)
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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