Ontology Population With Large Language Models (LLMs): A Case Study on Asbestos Ontology

Abstract

The Asbestos Ontology is a domain application ontology designed for use in an ontology-based approach that estimates the probability of the existence of asbestos products in a building. However, new issues in the building domain, such as predicting the presence of lead in buildings, renovating asbestos floors, or the reuse and recycling of components or parts of buildings as part of the circular economy, require a generalization of this ontology to a building ontology. The lack of relevant data tends to make decision-making difficult. The purpose of our approach is to show how instance-level knowledge graphs can be populated without having to manually create hundreds of instances using large language models (LLMs) and prompt engineering. This paper introduces a novel method for populating ontologies using the latest generative LLMs, such as GPT-3.5. Our method is characterized by an innovative recursive zero-shot prompting technique. The key contributions of this study are: (i) a new strategy for recursively prompting LLMs to elicit pertinent knowledge from the asbestos application domain; (ii) ontology population informed by the ontology metamodel; and (iii) formalization of the results into OWL axioms for the automatic integration of new instances. To evaluate the efficacy of our approach, we employed two main methodologies: (1) querying for instances linked to each entity; and (2) recursively querying for instances to leverage our recursive prompting strategy. Our initial strategy focused on evaluating the effectiveness of zero-shot prompting in retrieving relevant values for entities and data properties. This was facilitated through the development of the PromptGeneration function, which adjusted the input $C_i$ across various contexts.