Link prediction (LP) is crucial for Knowledge Graphs (KG) completion but commonly suffers from interpretability issues. While several methods have been proposed to explain embedding-based LP models, they are generally limited to local explanations on KG and are deficient in providing human interpretable semantics. Based on real-world observations of the characteristics of KGs from multiple domains, we propose to explain LP models in KG with path-based explanations. An integrated framework, namely eXpath, is introduced which incorporates the concept of relation path with ontological closed path rules to enhance both the efficiency and effectiveness of LP interpretation. Notably, the eXpath explanations can be fused with other single-link explanation approaches to achieve a better overall solution. Extensive experiments across benchmark datasets and LP models demonstrate that introducing eXpath can boost the quality of resulting explanations by about 20% on two key metrics and reduce the required explanation time by 61.4%, in comparison to the best existing method. Case studies further highlight eXpath's ability to provide more semantically meaningful explanations through path-based evidence.

翻译：链接预测（LP）对于知识图谱（KG）补全至关重要，但通常存在可解释性问题。虽然已有多种方法被提出来解释基于嵌入的LP模型，但这些方法通常局限于对KG的局部解释，且在提供人类可理解的语义方面存在不足。基于对多领域真实世界知识图谱特性的观察，我们提出使用基于路径的解释来阐明KG中的LP模型。本文引入了一个集成框架——eXpath，该框架将关系路径的概念与本体闭合路径规则相结合，以提升LP解释的效率和效果。值得注意的是，eXpath的解释可与其他单链接解释方法融合，以获得更优的整体解决方案。在多个基准数据集和LP模型上的大量实验表明，相较于现有最佳方法，引入eXpath可将关键解释质量指标提升约20%，并将所需解释时间减少61.4%。案例研究进一步凸显了eXpath通过基于路径的证据提供更具语义意义解释的能力。