Large Language Model Enhanced Concept Extraction Method for Ontology Learning

Information retrieval systems use ontologies to assist with document processing, but developing ontologies is knowledge-intensive and require detailed analysis of domain-specific knowledge. This paper addresses the limitations in ontology development research. It introduces a novel LLM based method called CESR-OL for automated concept extraction task to improve accuracy and domain relevance. A design science approach is adopted for this study. Evaluation will compare CESR-OL's performance against traditional machine learning methods. The study anticipates that CESR-OL will outperform existing methods, demonstrating the potential of LLMs for concept extraction in ontology learning and enabling more automated, accurate, and contextually aware ontologies.