Navigating Ontology Development with Large Language Models: A Study on User Stories Modelling

Ontology engineering is a complex and time-consuming task, even with the help of current modelling environments. Often the result is error-prone unless developed by experienced ontology engineers. However, with the emergence of new tools, such as generative AI, inexperienced modellers might receive assistance. This study investigates the capability of Large Language Models (LLMs) to generate OWL ontologies directly from ontological requirements. Specifically, our research question centres on the potential of LLMs in assisting human modellers, by generating OWL modelling suggestions and alternatives. We experiment with several state-of-the-art models. Our methodology incorporates diverse prompting techniques like Chain of Thoughts (CoT), Graph of Thoughts (GoT), and Decomposed Prompting, along with the Zero-shot method. Results show that currently, GPT-4 is the only model capable of providing suggestions of sufficient quality, and we also note the benefits and drawbacks of the prompting techniques. Overall, we conclude that it seems feasible to use advanced LLMs to generate OWL suggestions, which are at least comparable to the quality of human novice modellers. Our research is a pioneering contribution in this area, being the first to systematically study the ability of LLMs to assist ontology engineers.