用大模型根据需求自动扩展知识图谱，减少人工错误。
OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs
- 基于需求问题和检索增强生成，实现精准扩展
- 在39个需求测试中全部通过功能验证，结构缺陷少
- 适合企业级知识库维护者快速构建可复用模型
本研究提出OntoExtend框架，用于在实际需求驱动下扩展现有知识图谱。该框架利用检索增强生成技术，结合相关本体和以能力问题形式表达的需求，生成有依据的扩展内容。在两个真实场景中进行评估：欧盟项目公开本体Onto-DESIDE和博世工业本体，共覆盖39个能力问题。生成的本体片段结构问题极少，所有功能测试均通过，且被本体工程师评价为只需少量至中等程度修改即可集成。结果表明，OntoExtend可作为现实场景中需求驱动型本体扩展的起草助手，同时保持对能力问题特异性和建模风格的敏感性。
原文摘要 · Abstract (English)
Ontology extension refers to the process of enriching an existing ontology in response to emerging requirements, making it more complete. This task is a resource-intensive and error-prone process. 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 uses 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.
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