We propose the LLMs4OL approach, which utilizes Large Language Models (LLMs) for Ontology Learning (OL). LLMs have shown significant advancements in natural language processing, demonstrating their ability to capture complex language patterns in different knowledge domains. Our LLMs4OL paradigm investigates the following hypothesis: \textit{Can LLMs effectively apply their language pattern capturing capability to OL, which involves automatically extracting and structuring knowledge from natural language text?} To test this hypothesis, we conduct a comprehensive evaluation using the zero-shot prompting method. We evaluate nine different LLM model families for three main OL tasks: term typing, taxonomy discovery, and extraction of non-taxonomic relations. Additionally, the evaluations encompass diverse genres of ontological knowledge, including lexicosemantic knowledge in WordNet, geographical knowledge in GeoNames, and medical knowledge in UMLS.

翻译：我们提出LLMs4OL方法，该方法利用大语言模型进行本体学习。大语言模型在自然语言处理领域取得了显著进展，展示了其在不同知识领域中捕捉复杂语言模式的能力。我们的LLMs4OL范式探究以下假设：\textit{大语言模型能否将其语言模式捕获能力有效应用于本体学习——即从自然语言文本中自动提取并结构化知识？}为验证此假设，我们采用零样本提示方法进行了全面评估。针对术语分类、分类体系发现及非分类关系抽取三项本体学习核心任务，我们评估了九个不同的大语言模型家族。此外，评估涵盖多种本体知识类型，包括WordNet中的词汇语义知识、GeoNames中的地理知识以及UMLS中的医学知识。