🤖 AI Summary
This study addresses the challenge of high costs associated with manual ontology construction in specialized domains, which often results in a lack of authoritative reference resources. It presents the first systematic exploration of leveraging large language models (LLMs) for automated domain ontology development. Focusing on Brazil’s “Blue Amazon” maritime territory as a case study, the authors employ prompt engineering to guide GPT-3.5 and GPT-4 in emulating domain experts, enabling the automatic generation of structured concept hierarchies from initial seed concepts. The experimental pipeline produced twenty ontologies, which expert evaluators deemed largely coherent and logically organized. Although minor human refinement remains necessary, the results strongly demonstrate the feasibility and practical potential of using LLMs as virtual experts to support ontology construction in knowledge-intensive domains.
📝 Abstract
Ontologies are useful structures to organize and maintain information that can be understood both by humans and systems. However, since their manual crafting is a laborious task, many specific domains lack reference ontologies. The outstanding ability for understanding natural language demonstrated by the Large Language Models (LLMs) has motivated their application to aid on a variety of fields, including on ontology development. This work presents the experimentation with a technique that uses LLMs in the role of domain experts to build conceptual hierarchies for a given initial concept. Twenty ontologies automatically constructed for the domain of the Brazilian maritime territory (a.k.a the Blue Amazon) using GPT-3.5 and GPT-4 were then evaluated by human experts. The models were able to construct overall coherent conceptualizations of the domain, but none of the outputs was completely satisfactory as a representation of the context without refinement.