Abstract
This paper explores the integration of Large Language Models (LLMs) such as GPT-3.5 and GPT-4 into the ontology refinement process, specifically focusing on the OntoClean methodology. OntoClean, critical for assessing the metaphysical quality of ontologies, involves a two-step process of assigning meta-properties to classes and verifying a set of constraints. Manually conducting the first step proves difficult in practice, due to the need for philosophical expertise and lack of consensus among ontologists. By employing LLMs with two prompting strategies, the study demonstrates that high accuracy in the labelling process can be achieved. The findings suggest the potential for LLMs to enhance ontology refinement, proposing the development of plugin software for ontology tools to facilitate this integration.
Abstract (translated)
本文探讨了将诸如GPT-3.5和GPT-4等大型语言模型（LLMs）融入元语优化过程的具体内容，特别是关注OntoClean方法论。OntoClean方法对于评估元语的质量至关重要，它包括将元属性分配给类和验证一组约束的两个步骤。在实践中，手动进行第一步证明很难，因为需要哲学专业知识，不同元理论家之间存在分歧。通过采用具有两种提示策略的LLM，这项研究证明了在实验室中实现高准确标注过程是可能的。研究结果表明，LLM可以在元语优化中提高准确性，建议为元语工具开发插件软件，以促进这一整合。
URL
https://arxiv.org/abs/2403.15864
