NeurOWL: An LLM-Based Neural-symbolic Framework for Incomplete OWL Ontology Reasoning

2026-07-17

AI Summary

This work addresses the challenge of subsumption reasoning under incomplete OWL ontologies in real-world settings by proposing NeurOWL, a novel framework that unifies subsumption validation with open-set ontological abduction without requiring a pre-defined candidate set of missing ax日消息. NeurOWL integrates textual semantics from large language models, ontology embeddings, and neuro-symbolic reasoning to jointly leverage formal logic and linguistic knowledge for end-to-end inference. Experimental results demonstrate that NeurOWL achieves strong generalization and robust reasoning performance across multiple real-world domain ontologies, offering interpretable logical support for assessing semantic plausibility in the presence of ontological incompleteness.

Abstract

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.

Problem

• incomplete ontology
• subsumption reasoning
• ontology abduction
• semantic plausibility
• missing axioms

Innovation

• neuro-symbolic reasoning
• OWL ontology
• subsumption reasoning
• ontology abduction
• large language models