Knowledge Graphs organize information as entity-relation-entity triples, enabling machine learning models to predict plausible missing triples in a task known as Knowledge Graph Completion (KGC). Post-hoc explainability for KGC addresses the problem of identifying which triples most influence the predictions of machine learning models. Currently, the field lacks formalization and consistent evaluations, hindering reproducibility and cross-study comparisons. This paper argues for a unified taxonomy for post-hoc explainability in KGC. First, we propose a characterization of post-hoc explanations via multi-objective optimization that unifies existing post-hoc explainability algorithms in KGC and the explanations they produce, balancing explanation effectiveness and conciseness. Next, we examine improved evaluation protocols based on popular metrics, such as Mean Reciprocal Rank and Hits@k, through illustrative experiments. Finally, we stress the importance of interpretability as the ability of explanations to address queries meaningful to end users. By unifying methods and discussing evaluation standards, this work puts forward a case for more reproducible and impactful research in KGC explainability.

翻译：知识图谱以实体-关系-实体三元组的形式组织信息，使机器学习模型能够预测可能的缺失三元组，即知识图谱补全（KGC）任务。KGC的事后可解释性旨在识别哪些三元组对机器学习模型的预测影响最大。当前该领域缺乏形式化定义与一致性评估，阻碍了可重复性与跨研究比较。本文论证了KGC事后可解释性统一分类法的必要性。首先，我们提出通过多目标优化对事后解释进行特征化，统一了KGC中现有的事后解释算法及其生成的解释，在解释有效性与简洁性之间取得平衡。接着，基于流行指标（如平均倒数排名和Hits@k），通过说明性实验检验了改进的评估协议。最后，我们强调可解释性的重要性在于解释能够回答终端用户关注的查询。通过统一方法并讨论评估标准，本研究为提升KGC可解释性研究的可重复性与影响力提供了论证。