To achieve a flexible and adaptable system, capability ontologies are increasingly leveraged to describe functions in a machine-interpretable way. However, modeling such complex ontological descriptions is still a manual and error-prone task that requires a significant amount of effort and ontology expertise. This contribution presents an innovative method to automate capability ontology modeling using Large Language Models (LLMs), which have proven to be well suited for such tasks. Our approach requires only a natural language description of a capability, which is then automatically inserted into a predefined prompt using a few-shot prompting technique. After prompting an LLM, the resulting capability ontology is automatically verified through various steps in a loop with the LLM to check the overall correctness of the capability ontology. First, a syntax check is performed, then a check for contradictions, and finally a check for hallucinations and missing ontology elements. Our method greatly reduces manual effort, as only the initial natural language description and a final human review and possible correction are necessary, thereby streamlining the capability ontology generation process.

翻译：为实现灵活且适应性强的系统，能力本体日益被用于以机器可解释的方式描述功能。然而，对此类复杂本体论描述进行建模仍是一项手动且易出错的任务，需要投入大量精力并具备本体论专业知识。本文提出一种利用大语言模型（LLMs）实现能力本体建模自动化的创新方法，LLMs已被证明非常适合此类任务。我们的方法仅需提供能力的自然语言描述，随后通过少量样本提示技术将其自动插入预定义的提示模板中。在向LLM发出提示后，生成的能力本体会在LLM参与的循环中通过多个步骤自动验证，以检查能力本体的整体正确性：首先执行语法检查，继而检查矛盾，最后检查幻觉及缺失的本体元素。我们的方法大幅减少了人工工作量，仅需初始自然语言描述及最终的人工审查与可能的修正，从而简化了能力本体的生成流程。