Enhancing Ontologies with Large Language Models: A Semi-Automated Approach

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dc.contributor.authorPham, Tran Van Anh
dc.contributor.authorHuettemann, Sebastian
dc.contributor.authorMueller, Roland M.
dc.date.accessioned2024-12-26T21:05:44Z
dc.date.available2024-12-26T21:05:44Z
dc.date.issued2025-01-07
dc.description.abstractThe process of creating and maintaining domain ontologies is a time- and resource-intensive activity, given the dynamic nature of domain knowledge and the regular introduction of new terms. This study aims to determine the effectiveness of large language models (LLMs) in augmenting the domain ontology authoring process. We fine-tuned state-of-the-art pre-trained LLMs and evaluated their performance on two tasks: synonym identification and parent-child relationship identification. The models achieved 98% accuracy in the first task and 75.4% accuracy in the second, demonstrating significant capabilities in automating synonym identification and relationship classification. In addition to providing a methodological basis for further extending and improving these results, we demonstrate that LLMs can be effectively used in ontology development and maintenance. This can save time and effort in the process.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2025.189
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.other5eb3afce-38c9-4f91-ae2a-ab1ad2e2efb4
dc.identifier.urihttps://hdl.handle.net/10125/109029
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectNatural Language Processing and Large Language Models Supporting Data Analytics for System Sciences
dc.subjectlarge language models, natural language processing, ontology enrichment, transformer models
dc.titleEnhancing Ontologies with Large Language Models: A Semi-Automated Approach
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage1561

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