Semi-Automatic Domain Ontology Construction: LLMs, Modularization, and Cognitive Representation

Silvia Lucia Borowicc, Solange Alves-Souza, Solange Alves-Souza

2025

Abstract

Domain ontology construction is a complex and resource-intensive task, traditionally relying on extensive manual effort from ontology engineers and domain experts. While Large Language Models (LLMs) show promise for automating parts of this process, studies indicate they often struggle with capturing domain-specific nuances, maintaining ontological consistency, and identifying subtle relationships, frequently requiring significant human curation. This paper presents a semi-automatic method for domain ontology construction that combines the capabilities of LLMs with established ontology engineering practices, modularization, and cognitive representation. We developed a pipeline incorporating semantic retrieval from heterogeneous document collections, and prompt-guided LLM generation. Two distinct scenarios were evaluated to assess the influence of prior structured knowledge: one using only retrieved document content as input, and another incorporating expert-defined structured seed terms alongside document content. The approach was applied to the domain of Dengue surveillance and control, and the resulting ontologies were evaluated based on structural metrics and logical consistency. Results showed that the scenario incorporating expert-defined seed terms yielded ontologies with greater conceptual coverage, deeper hierarchies and improved cognitive representation compared to the scenario without prior structured knowledge. We also observed significant performance variations between different LLM models regarding their ability to capture semantic details and structure complex domains. This work demonstrates the viability and benefits of a hybrid approach for ontology construction, highlighting the crucial role of combining LLMs with human expertise for more efficient, consistent, and cognitively aligned ontology engineering. The findings support an iterative and incremental ontology development process and suggest LLMs are valuable assistants when guided by domain-specific inputs and integrated into a structured methodology.

Paper Citation

in Harvard Style

Borowicc S. and Alves-Souza S. (2025). Semi-Automatic Domain Ontology Construction: LLMs, Modularization, and Cognitive Representation. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD; ISBN 978-989-758-769-6, SciTePress, pages 64-73. DOI: 10.5220/0013718000004000

in Bibtex Style

@conference{keod25,
author={Silvia Borowicc and Solange Alves-Souza},
title={Semi-Automatic Domain Ontology Construction: LLMs, Modularization, and Cognitive Representation},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD},
year={2025},
pages={64-73},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013718000004000},
isbn={978-989-758-769-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD
TI - Semi-Automatic Domain Ontology Construction: LLMs, Modularization, and Cognitive Representation
SN - 978-989-758-769-6
AU - Borowicc S.
AU - Alves-Souza S.
PY - 2025
SP - 64
EP - 73
DO - 10.5220/0013718000004000
PB - SciTePress