A Short Review for Ontology Learning: Stride to Large Language Models Trend

TL;DR

This paper reviews ontology learning methods, highlighting recent trends of using large language models to improve knowledge extraction, and discusses challenges, methodologies, limitations, and future directions in this evolving field.

Contribution

It provides a comprehensive review of ontology learning approaches, emphasizing the emerging use of large language models and proposing future research directions.

Findings

Analyzes shallow and deep learning techniques for ontology learning.

Highlights the potential of large language models to enhance ontology construction.

Discusses challenges and limitations in current methodologies.

Abstract

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.