Paper Type
ERF
Abstract
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.
Paper Number
2341
Recommended Citation
Perera, Olga; Hou, Benjamin X.; and Liu, Jun, "Large Language Model Enhanced Concept Extraction Method for Ontology Learning" (2025). AMCIS 2025 Proceedings. 15.
https://aisel.aisnet.org/amcis2025/sig_odis/sig_odis/15
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.
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