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HoGRN: Explainable Sparse Knowledge Graph Completion via High-Order Graph Reasoning Network
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DOI：10.1109/TKDE.2024.3422226.png)
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Abstract
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Knowledge Graphs (KGs) are becoming increasingly essential infrastructures in many applications while suffering from incompleteness issues. The KG Completion (KGC) task 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 (GCN), 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. Two main components 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 range of missing facts. Second, the entity updating component leverages a weight-free GCN to efficiently model KG structures with interpretability. For evaluation, we conduct extensive experiments-the results of HoGRN on several sparse KGs present considerable improvements. Further ablation and case studies demonstrate the effectiveness of the main components.
Keywords:
Cognition
Tail
Vectors
Task analysis
Semantics
Reinforcement learning
Knowledge graphs
Knowledge graph
link prediction
relational reasoning
graph convolutional network
interpretability
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