Capability ontologies are increasingly used to model functionalities of systems or machines. The creation of such ontological models with all properties and constraints of capabilities is very complex and can only be done by ontology experts. However, Large Language Models (LLMs) have shown that they can generate machine-interpretable models from natural language text input and thus support engineers / ontology experts. Therefore, this paper investigates how LLMs can be used to create capability ontologies. We present a study with a series of experiments in which capabilities with varying complexities are generated using different prompting techniques and with different LLMs. Errors in the generated ontologies are recorded and compared. To analyze the quality of the generated ontologies, a semi-automated approach based on RDF syntax checking, OWL reasoning, and SHACL constraints is used. The results of this study are very promising because even for complex capabilities, the generated ontologies are almost free of errors.

翻译：能力本体被日益广泛地用于建模系统或机器的功能。创建包含能力所有属性和约束的本体模型极为复杂，且只能由本体专家完成。然而，大语言模型已证明其能够从自然语言文本输入中生成机器可解释的模型，从而辅助工程师/本体专家。为此，本文探讨如何利用大语言模型创建能力本体。我们通过一系列实验开展研究，采用不同提示技术及多种大语言模型生成复杂度各异的能力，记录并比较生成本体中的错误。为分析所生成本体的质量，采用基于RDF语法检查、OWL推理与SHACL约束的半自动化方法。研究结果十分乐观——即便是复杂能力，生成的本体也几乎无错误。