Enhancing Domain Modeling with Pre-trained Large Language Models: An Automated Assistant for Domain Modelers

Dominik Prokop, Stepán Stenchlák, Petr Skoda, Jakub Klímek, Martin Necaský

Abstract: Domain modeling involves creating abstract representations of information within a specific domain using techniques such as conceptual modeling and ontology engineering. Traditionally, manual creation and maintenance of domain models are labor intensive and require modeling expertise. This paper explores the automation of domain modeling using pre-trained large language models (LLMs), presenting an experimental LLM-based conceptual modeling assistant that collaborates with a human expert. The assistant provides modeling suggestions based on a given textual description of the domain of interest, aiding in the design of classes, attributes, and associations. We present a generic framework for domain modeling assistants that consists of class, attribute, and association generators, and show how they can be implemented using an LLM. We demonstrate a concrete configuration of this framework and its prototype implementation. We evaluated the effectiveness of the framework configuration across various domains. Our findings indicate that the assistant significantly enhances the efficiency of modeling while maintaining reasonable quality of the outputs.

External IDs:dblp:conf/er/ProkopSSKN24