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：能力本体日益被用于对系统或机器功能进行建模。然而，构建包含所有能力属性与约束的本体模型极为复杂，且通常仅能由本体专家完成。大语言模型已展现出从自然语言文本输入生成机器可解释模型的能力，从而能够辅助工程师/本体专家。因此，本文探究如何利用大语言模型生成能力本体。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本体，并记录与比较生成本体中的错误。为分析生成本体的质量，我们采用基于RDF语法检查、OWL推理及SHACL约束的半自动化方法。研究结果表明：即使针对复杂能力，生成的本体也几乎无错误，这一成果极具前景。