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.

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