Article: Development of ontological knowledge bases by leveraging large language models Journal: International Journal of Knowledge Management Studies (IJKMS) 2026 Vol.17 No.2 pp.172 - 206 Abstract: Ontological Knowledge Bases (OKBs) play a vital role in structuring domain-specific knowledge and serve as a foundation for effective knowledge management systems (KMS). However, their traditional manual development poses significant challenges related to scalability, consistency, and adaptability. Recent advancements in generative AI, particularly large language models (LLMs), offer promising solutions for automating and enhancing OKB development. This paper introduces a structured, iterative methodology leveraging LLMs to optimise knowledge acquisition, automate ontology artefact generation, and enable continuous refinement cycles. We demonstrate this approach through a detailed case study focused on developing a user context profile ontology within the vehicle sales domain. Key contributions include significantly accelerated ontology construction processes, improved ontological consistency, effective bias mitigation, and enhanced transparency in the ontology engineering process. Our findings highlight the transformative potential of integrating LLMs into ontology development, notably improving scalability, integration capabilities, and overall efficiency in knowledge management systems. Inderscience Publishers - linking academia, business and industry through research

Title: Development of ontological knowledge bases by leveraging large language models

Authors: Ngoc Luyen Le; Marie-Hélène Abel; Philippe Gouspillou

Addresses: Université de technologie de Compiègne, CNRS, Heudiasyc (Heuristics and Diagnosis of Complex Systems), CS 60319 – 60203 Compiègne Cedex, France ' Université de technologie de Compiègne, CNRS, Heudiasyc (Heuristics and Diagnosis of Complex Systems), CS 60319 – 60203 Compiègne Cedex, France ' Vivocaz, 8 B Rue de la Gare, 02200, Mercin-et-Vaux, France

Abstract: Ontological Knowledge Bases (OKBs) play a vital role in structuring domain-specific knowledge and serve as a foundation for effective knowledge management systems (KMS). However, their traditional manual development poses significant challenges related to scalability, consistency, and adaptability. Recent advancements in generative AI, particularly large language models (LLMs), offer promising solutions for automating and enhancing OKB development. This paper introduces a structured, iterative methodology leveraging LLMs to optimise knowledge acquisition, automate ontology artefact generation, and enable continuous refinement cycles. We demonstrate this approach through a detailed case study focused on developing a user context profile ontology within the vehicle sales domain. Key contributions include significantly accelerated ontology construction processes, improved ontological consistency, effective bias mitigation, and enhanced transparency in the ontology engineering process. Our findings highlight the transformative potential of integrating LLMs into ontology development, notably improving scalability, integration capabilities, and overall efficiency in knowledge management systems.

Keywords: ontology development; ontological knowledge bases; OKBs; large language models; LLMs; knowledge representation; user modelling; knowledge management.

DOI: 10.1504/IJKMS.2026.153906

International Journal of Knowledge Management Studies, 2026 Vol.17 No.2, pp.172 - 206

Accepted: 17 Dec 2025
Published online: 29 May 2026 *

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Title: Development of ontological knowledge bases by leveraging large language models

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