  Ontologies provide formal representation of knowledge shared within Semantic
Web applications. Ontology learning involves the construction of ontologies
from a given corpus. 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 (LLMs) 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 LLMs to enhance ontology learning. In addition, it proposes
several noteworthy future directions for further exploration into the
integration of LLMs with ontology learning tasks.
