用路径规则解释知识图谱链接预测，让模型决策更可懂。
eXpath: Explaining Knowledge Graph Link Prediction with Ontological Closed Path Rules
- 基于本体闭合路径规则生成可读的解释路径
- 解释质量提升约20%，推理时间减少61.4%
- 适合需要可解释性的人工智能应用
链接预测（LP）对知识图谱（KG）补全至关重要，但普遍存在可解释性差的问题。现有方法多局限于局部解释，缺乏人类可理解的语义。基于多领域知识图谱的实际特征，本文提出基于路径的解释方法，构建集成框架eXpath，融合关系路径与本体闭合路径规则，显著提升解释效率与效果。eXpath解释可与其他单链接解释方法融合，形成更优整体方案。在多个基准数据集和LP模型上的实验表明，引入eXpath使解释质量在两个关键指标上平均提升约20%，解释时间减少61.4%。案例研究进一步验证了其通过路径证据提供更语义化的解释能力。
原文摘要 · Abstract (English)
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.
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