Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web. Although knowledge graph embedding (KGE) models have demonstrated superior predictive performance on KGC tasks, these models infer missing links in a black-box manner that lacks transparency and accountability, preventing researchers from developing accountable models. Existing KGE-based explanation methods focus on exploring key paths or isolated edges as explanations, which is information-less to reason target prediction. Additionally, the missing ground truth leads to these explanation methods being ineffective in quantitatively evaluating explored explanations. To overcome these limitations, we propose KGExplainer, a model-agnostic method that identifies connected subgraph explanations and distills an evaluator to assess them quantitatively. KGExplainer employs a perturbation-based greedy search algorithm to find key connected subgraphs as explanations within the local structure of target predictions. To evaluate the quality of the explored explanations, KGExplainer distills an evaluator from the target KGE model. By forwarding the explanations to the evaluator, our method can examine the fidelity of them. Extensive experiments on benchmark datasets demonstrate that KGExplainer yields promising improvement and achieves an optimal ratio of 83.3% in human evaluation.

翻译：知识图谱补全（KGC）旨在缓解知识图谱（KG）固有的不完整性，这对于网络推荐等各类应用而言是一项关键任务。尽管知识图谱嵌入（KGE）模型在KGC任务上已展现出优越的预测性能，但这些模型以缺乏透明性和可解释性的黑箱方式推断缺失链接，阻碍了研究者开发可解释的模型。现有基于KGE的解释方法主要聚焦于探索关键路径或孤立边作为解释，这些方法缺乏信息量，难以推理目标预测。此外，缺失的真实标注导致这些解释方法无法对探索得到的解释进行定量评估。为克服上述局限，我们提出KGExplainer——一种模型无关方法，它能够识别连通子图解释，并蒸馏出评价器对其进行定量评估。KGExplainer采用基于扰动的贪心搜索算法，在目标预测的局部结构中寻找关键连通子图作为解释。为评估所探索解释的质量，KGExplainer从目标KGE模型中蒸馏出评价器。通过将解释输入评价器，我们的方法可以检验其保真度。在基准数据集上的大量实验表明，KGExplainer取得了显著的性能提升，并在人工评估中达到了83.3%的最优比率。