Knowledge Graphs (KGs) are becoming increasingly essential infrastructures in many applications while suffering from incompleteness issues. The KG completion task (KGC) automatically predicts missing facts based on an incomplete KG. However, existing methods perform unsatisfactorily in real-world scenarios. On the one hand, their performance will dramatically degrade along with the increasing sparsity of KGs. On the other hand, the inference procedure for prediction is an untrustworthy black box. This paper proposes a novel explainable model for sparse KGC, compositing high-order reasoning into a graph convolutional network, namely HoGRN. It can not only improve the generalization ability to mitigate the information insufficiency issue but also provide interpretability while maintaining the model's effectiveness and efficiency. There are two main components that are seamlessly integrated for joint optimization. First, the high-order reasoning component learns high-quality relation representations by capturing endogenous correlation among relations. This can reflect logical rules to justify a broader of missing facts. Second, the entity updating component leverages a weight-free Graph Convolutional Network (GCN) to efficiently model KG structures with interpretability. Unlike conventional methods, we conduct entity aggregation and design composition-based attention in the relational space without additional parameters. The lightweight design makes HoGRN better suitable for sparse settings. For evaluation, we have conducted extensive experiments-the results of HoGRN on several sparse KGs present impressive improvements (9% MRR gain on average). Further ablation and case studies demonstrate the effectiveness of the main components. Our codes will be released upon acceptance.

翻译：知识图表(KGs)在许多应用中越来越成为基本的基础设施,同时受到不完全问题的困扰。 KG的完成任务(KGC)自动预测基于不完整的KG的缺失事实。 然而,现有的方法在现实世界情景中表现不令人满意。 一方面, 其性能将随着KGs日益松散而急剧下降。 另一方面, 预测的推论程序是一个不可信的黑盒。 本文为稀疏的KGC提出一个新的可解释模式, 将高阶推理纳入平面变迁网络, 即HGRN。 它不仅可以提高一般化能力, 以缓解信息不足问题, 而且提供解释性能, 同时保持模型的效能和效率。 一方面, 它们的性能将随着KGs的日益松散而大幅下降。 首先, 高阶推论部分通过吸收内在的相互关系来学习高质量的关系。 这可以反映逻辑规则,以证明缺少的更多事实是合理的。 其次, 实体更新后将利用无重量的变迁网络(GCN) 来减轻信息不充分化问题的能力, 并且能够更好地解释模型的KGrormal 结构。