Competency Questions (CQs) are a cornerstone of requirement elicitation in ontology engineering. CQs represent requirements as a set of natural language questions that an ontology should satisfy; they are traditionally modelled by ontology engineers together with domain experts as part of a human-centred, manual elicitation process. The use of Generative AI automates CQ creation at scale, therefore democratising the process of generation, widening stakeholder engagement, and ultimately broadening access to ontology engineering. However, given the large and heterogeneous landscape of LLMs, varying in dimensions such as parameter scale, task and domain specialisation, and accessibility, it is crucial to characterise and understand the intrinsic, observable properties of the CQs they produce (e.g., readability, structural complexity) through a systematic, cross-domain analysis. This paper introduces a set of quantitative measures for the systematic comparison of CQs across multiple dimensions. Using CQs generated from well defined use cases and scenarios, we identify their salient properties, including readability, relevance with respect to the input text and structural complexity of the generated questions. We conduct our experiments over a set of use cases and requirements using a range of LLMs, including both open (KimiK2-1T, LLama3.1-8B, LLama3.2-3B) and closed models (Gemini 2.5 Pro, GPT 4.1). Our analysis demonstrates that LLM performance reflects distinct generation profiles shaped by the use case.

翻译：胜任力问题（Competency Questions, CQs）是本体工程中需求获取的基石。CQ以本体应满足的自然语言问题集形式表达需求；传统上，它们由本体工程师与领域专家共同建模，作为以人为中心、人工驱动的需求获取过程的一部分。生成式人工智能的应用实现了大规模CQ的自动化创建，从而推动了生成过程的民主化，扩大了利益相关者的参与范围，并最终拓宽了本体工程的普及程度。然而，鉴于大语言模型（LLM）的广泛性与异质性——其在参数规模、任务与领域专精化程度及可访问性等维度上存在差异——通过系统性的跨领域分析来刻画并理解LLM所生成CQ的内在可观测属性（如可读性、结构复杂度）至关重要。本文引入了一套定量指标，用于从多个维度对CQ进行系统性比较。我们基于从明确界定的用例与场景生成的CQ，识别了其显著属性，包括可读性、与输入文本的相关性以及生成问题的结构复杂度。我们使用一系列LLM（包括开源模型KimiK2-1T、LLama3.1-8B、LLama3.2-3B与闭源模型Gemini 2.5 Pro、GPT 4.1），针对一组用例与需求开展了实验。分析结果表明，LLM的性能体现了受用例塑造的差异化生成特征。