NeurOWL: An LLM-Based Neural-symbolic Framework for Incomplete OWL Ontology Reasoning
For ontology engineers and AI systems relying on incomplete knowledge bases, NeurOWL provides a practical solution to subsumption reasoning without requiring predefined missing axiom candidates.
NeurOWL addresses subsumption reasoning in incomplete OWL ontologies by jointly verifying plausibility and generating explanations with missing axioms. It achieves strong and robust performance across multiple real-world domains.
OWL ontologies provide a formal knowledge representation framework that enables semantic reasoning, and have been widely adopted across domains such as healthcare and bioinformatics. In practice, however, real-world ontologies are often incomplete, which pose challenges for reasoning. In this work, we focus on a fundamental subsumption reasoning problem: given an incomplete ontology and a candidate (non-entailed) subsumption, determine whether the subsumption is semantically plausible and, if so, providing a logically sound explanation containing potential missing axioms. This task unifies subsumption verification with ontology abduction, and generalizes the latter by removing the need for a predefined candidate set of missing axioms. To address this subsumption reasoning problem, we propose NeurOWL, an end-to-end neuro-symbolic framework that jointly performs verification and abduction, leveraging both formally defined semantics and textual semantics through Large Language Models and ontology embeddings. We evaluate NeurOWL on real-world ontologies across multiple domains, demonstrating strong and robust performance across different domains.