Graph Neural Networks (GNNs) have achieved great success in Knowledge Graph Completion (KGC) by modelling how entities and relations interact in recent years. However, the explanation of the predicted facts has not caught the necessary attention. Proper explanations for the results of GNN-based KGC models increase model transparency and help researchers develop more reliable models. Existing practices for explaining KGC tasks rely on instance/subgraph-based approaches, while in some scenarios, paths can provide more user-friendly and interpretable explanations. Nonetheless, the methods for generating path-based explanations for KGs have not been well-explored. To address this gap, we propose Power-Link, the first path-based KGC explainer that explores GNN-based models. We design a novel simplified graph-powering technique, which enables the generation of path-based explanations with a fully parallelisable and memory-efficient training scheme. We further introduce three new metrics for quantitative evaluation of the explanations, together with a qualitative human evaluation. Extensive experiments demonstrate that Power-Link outperforms the SOTA baselines in interpretability, efficiency, and scalability.

翻译：图神经网络（GNN）通过建模实体与关系之间的交互，近年来在知识图谱补全（KGC）任务中取得了显著成功。然而，预测结果的解释性尚未获得足够关注。针对基于GNN的KGC模型结果提供合理解释，能够提升模型透明度，并助力研究者开发更可靠的模型。当前KGC任务的解释实践主要依赖实例/子图方法，但在某些场景下，路径能提供更友好且可解释的说明。尽管如此，面向知识图谱的路径解释生成方法仍鲜有探索。为填补这一空白，我们提出Power-Link——首个探索GNN模型的路径式KGC解释器。我们设计了一种新颖的简化图幂技术，通过完全可并行化且内存高效的训练方案生成路径解释。同时引入三项新指标用于解释的定量评估，并辅以定性人工评估。大量实验表明，Power-Link在可解释性、效率与可扩展性上均优于当前最优基线方法。