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.

知识图谱补全（KGC）通过建立知识图谱嵌入（KGE）模型、探索连接子图解释方法和评估器等手段，有效改善知识图谱的不完整性问题，并在人类评估中取得了 83.3% 的最优比例。