Leveraging Knowledge Graphs inference for Semi-Explainable Systems based on Large Language Models

Recent advances in applying Large Language Models (LLMs) to natural language processing raise the challenge of integrating them with ontological models, to harness the features of Knowledge Graphs (KG) alongside the expressive abilities of LLMs. This paper introduces QuLIO-XR, a framework designed to integrate LLMs and ontologies, proposing an approach combining reasoning capabilities of OWL 2 with the expressive power of an LLM. Natural language text is structurally and semantically represented through the foundational ontology called LODO, which combines straightforward notation with human-like reasoning capabilities to address the issues derived from the expressive arbitrariness of natural languages. Experiments demonstrate also promising translation performances from RDF triples to the natural language, establishing QuLIO-XR as a valuable tool in the realm of LLMs explainability once fine-tuned with the same knowledge employed to build LODO KGs.