LLM-Based Modeling Assistance for Textual Ontology-Driven Conceptual Modeling

Large Language Models (LLMs) have already shown potentially significant capabilities in assisting users with writing and coding tasks. In this paper, we explore how LLM-based assistance can be leveraged in a modeling environment for textual Ontology-Driven Conceptual Modeling. We integrate the UFO-based textual language ‘Tonto’with an LLM-powered assistant. We employ detailed UFO-based ‘guidance’texts which are included by the modeling environment automatically in the context of user prompts along with the current ontology coding artifacts. The tool can take actions such as creating files, changing code, invoking Tonto syntax verification, while still maintaining the modeler in the loop. Our initial exploration shows that a number of modeling tasks can potentially be automated (such as suggesting new elements, summarizing the model, checking consistency of usage of UFO concepts, model fixing, etc.). The tool is proposed as a testbed for empirical user studies.