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。

LLMs4OL：用于本体学习的大型语言模型

LLMs4OL: Large Language Models for Ontology Learning

The quest for acquiring a formal representation of the knowledge of a domain
of interest has attracted researchers with various backgrounds into a diverse
field called ontology learning. We highlight classical machine learning and
data mining approaches that have been proposed for (semi-)automating the
creation of description logic (DL) ontologies. These are based on association
rule mining, formal concept analysis, inductive logic programming,
computational learning theory, and neural networks. We provide an overview of
each approach and how it has been adapted for dealing with DL ontologies.
Finally, we discuss the benefits and limitations of each of them for learning
DL ontologies.

该文介绍了应用于形式化知识表示领域的本体学习中的经典机器学习和数据挖掘方法，包括关联规则挖掘、正式概念分析、归纳逻辑编程、计算学习理论及神经网络，总结了各种方法在学习 DL 本体方面的优点和限制。

学习描述逻辑本体论。五种方法。它们现在在哪里？

Learning Description Logic Ontologies. Five Approaches. Where Do They  Stand?

Ontologies are a popular way of representing domain knowledge, in particular,
knowledge in domains related to life sciences. (Semi-)automating the process of
building an ontology has attracted researchers from different communities into
a field called "Ontology Learning". We provide a formal specification of the
exact and the probably approximately correct learning models from computational
learning theory. Then, we recall from the literature complexity results for
learning lightweight description logic (DL) ontologies in these models.
Finally, we highlight other approaches proposed in the literature for learning
DL ontologies.

本文介绍了用于生命科学领域相关知识表示的本体论。提供了从计算学习理论中学习模型的正式规范，同时回顾了在这些模型中学习轻量级描述逻辑（DL）本体论的复杂性结果，最后，我们强调了文献中提出的其他学习 DL 本体论的方法。