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.Comment: The manuscript under revie