用大模型解决知识图谱推理中缺失信息的补全问题
NeurOWL: An LLM-Based Neural-symbolic Framework for Incomplete OWL Ontology Reasoning
- 结合大模型与符号逻辑，自动判断不完整本体中的蕴含关系是否合理
- 在多个真实领域数据集上表现稳健，能准确识别并补全缺失公理
- 适合需要高精度知识推理的医疗、生物信息学等场景
OWL本体提供形式化知识表示框架，支持语义推理，在医疗、生物信息学等领域广泛应用。但实际应用中本体常不完整，影响推理效果。本文聚焦基础的蕴含推理问题：给定一个不完整的本体和一个候选非蕴含关系，判断该蕴含是否语义上合理，若合理则提供包含潜在缺失公理的逻辑解释。该任务融合蕴含验证与本体归纳，且无需预设缺失公理集合。为此，我们提出NeurOWL，一种端到端神经符号框架，联合执行验证与归纳，利用大语言模型和本体嵌入融合形式语义与文本语义。我们在跨多领域的真实本体上评估NeurOWL，结果表明其在不同领域均表现出强且稳健的性能。
原文摘要 · Abstract (English)
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.
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