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.

我们提出了 LLMs4OL 方法，利用大型语言模型（LLMs）进行本体学习（OL）。通过全面评估使用零训练样例提示方法，我们发现 LLMs 可以有效地应用其语言模式捕捉能力于 OL，该能力包括从自然语言文本中自动提取和结构化知识。评估涵盖了对三个主要的 OL 任务进行九种不同的 LLM 模型家族的评估，包括术语类型化，分类系统发现以及非分类关系的提取，并包含了 WordNet 中的词汇语义知识，GeoNames 中的地理知识以及 UMLS 中的医学知识等多种类型的本体知识。