Ontologies provide formal representation of knowledge shared within Semantic Web applications and Ontology learning from text involves the construction of ontologies from a given corpus of text. In the past years, ontology learning has traversed through shallow learning and deep learning methodologies, each offering distinct advantages and limitations in the quest for knowledge extraction and representation. A new trend of these approaches is relying on large language models to enhance ontology learning. This paper gives a review in approaches and challenges of ontology learning. It analyzes the methodologies and limitations of shallow-learning-based and deep-learning-based techniques for ontology learning, and provides comprehensive knowledge for the frontier work of using large language models to enhance ontology learning. In addition, it proposes several noteworthy future directions for further exploration into the integration of large language models with ontology learning tasks.

翻译：本体论为语义网应用中的共享知识提供了形式化表示，而从文本中学习本体则涉及从给定文本语料库构建本体。过去数年，本体学习经历了浅层学习和深度学习方法的演进，这两种方法在知识提取与表征方面各有其优势与局限。这些方法的新趋势是依赖大型语言模型来增强本体学习。本文综述了本体学习的方法与挑战，分析了基于浅层学习和深度学习的本体学习技术及其局限性，并全面介绍了利用大型语言模型增强本体学习的前沿研究成果。此外，本文还提出了若干值得关注的未来研究方向，以进一步探索大型语言模型与本休学习任务的融合。