发表机构
Linköping University; University of Bologna; Bosch; ISTC-CNR（林雪平大学; 博洛尼亚大学; 博世公司; 意大利国家研究委员会信息科学与技术研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究基于需求驱动的本体扩展问题，提出用检索增强生成的OntoExtend框架，通过在相关输入本体和需求上应用该框架，在两个用例的能力问题上评估，结果显示其可作为现实场景中本体扩展起草助手，对问题特异性和建模概要敏感。
AI 中文摘要
本体扩展是指根据新出现的需求丰富现有本体，使其更完整的过程。此任务资源密集且易出错。大语言模型在从头生成本体方面表现出良好性能，但当前方法很少将本体扩展与需求或可重用核心模型明确联系，对大语言模型输出的系统评估有限。本文介绍了OntoExtend，一个基于需求驱动的大语言模型本体扩展框架。它通过对相关输入本体和以能力问题形式呈现的需求使用检索增强生成来提出有根据的扩展。我们在来自两个用例（一个欧盟公共项目本体Onto-DESIDE和博世的一个工业本体）的39个能力问题上评估了OntoExtend。生成的片段结构问题少，满足所有功能评估测试，本体工程师认为在集成前只需进行小到中度修订。这些结果表明OntoExtend可作为现实世界场景中基于需求驱动的本体扩展的起草助手，同时对能力问题的特异性和建模概要敏感。
英文摘要
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.
CommentsAccepted in research track of Semantics 2026: https://2026-eu.semantics.cc/page/accepted_research
