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

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 dis...

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Bibliographic Details
Published inarXiv.org
Main Authors Du, Rick, An, Huilong, Wang, Keyu, Liu, Weidong
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 17.06.2024
Subjects
Applications programs
Cognitive tasks
Deep learning
Knowledge representation
Large language models
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Summary: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.
ISSN:2331-8422