LLM-Assisted Generation of SWRL Rules from Natural Language

Recently, Large Language Models (LLMs) have attracted great attention due to their remarkable performance in human-like text generation and reasoning skills (although their memory and hallucination problems still remain key issues to tackle more efficiently). LLMs have been applied to various application domains, including Knowledge Graph (KG) generation, question and answering over KGs and text-to-SPARQL translation. In this work, we investigate the capabilities of LLMs in text-to-SWRL translation, i.e., translation of Natural Language (NL) rules into Semantic Web Rule Language (SWRL) rules, put in the context of an industrial Ontology Engineering (OE) environment called GLUON, presenting our first experimental results. The aim of this work is to identify the level of automation that is adequate for the LLM to generate well-formed SWRL rules, towards the development of an LLM-based framework, as a plugin to the GLUON OE environment. In this direction we leverage and combine the reasoning capabilities of GPT-4o model, the Retrieval-Augmented Generation (RAG) technology, and prompt engineering. We employ quantitative and qualitative metrics to evaluate the generated SWRL rules, focusing on the correct syntax and the level of human intervention.