发表机构
The University of Manchester; Zhejiang University（曼彻斯特大学; 浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究不完整OWL本体的包含关系推理问题，提出NeurOWL框架，通过大语言模型和本体嵌入联合执行验证和溯因，利用形式与文本语义，在多领域真实本体上评估，展现强大稳健性能。
AI 中文摘要
OWL本体提供了一个支持语义推理的形式化知识表示框架，在医疗和生物信息学等领域广泛应用。然而在实际中，现实世界的本体往往不完整，给推理带来挑战。本文聚焦于一个基本的包含关系推理问题：给定一个不完整本体和一个候选（非蕴含）包含关系，判断该包含关系在语义上是否合理，若合理则提供包含潜在缺失公理的逻辑合理的解释。此任务将包含关系验证与本体溯因统一起来，并通过去除对预定义缺失公理候选集的需求对后者进行了推广。为解决此包含关系推理问题，我们提出了NeurOWL，一个端到端的神经符号框架，通过大语言模型和本体嵌入联合执行验证和溯因，利用形式定义语义和文本语义。我们在多个领域的真实世界本体上评估了NeurOWL，展示了其在不同领域的强大且稳健的性能。
英文摘要
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.
