Knowledge Graph Reasoning with Relational Digraph

Abstract

Reasoning on the knowledge graph (KG) aims to infer new facts from existing ones. Methods based on the relational path have shown strong, interpretable, and transferable reasoning ability. However, paths are naturally limited in capturing local evidence in graphs. In this paper, we introduce a novel relational structure, i.e., relational directed graph (r-digraph), which is composed of overlapped relational paths, to capture the KG's local evidence. Since the r- digraphs are more complex than paths, how to efficiently construct and effectively learn from them are challenging. Directly encoding the r-digraphs cannot scale well and capturing query-dependent information is hard in r-digraphs. We propose a variant of graph neural network, i.e., RED-GNN, to address the above challenges. Specifically, RED-GNN makes use of dynamic programming to recursively encodes multiple r-digraphs with shared edges, and utilizes a query-dependent attention mechanism to select the strongly correlated edges. We demonstrate that RED-GNN is not only efficient but also can achieve significant performance gains in both inductive and transductive reasoning tasks over existing methods. Besides, the learned attention weights in RED-GNN can exhibit interpretable evidence for KG reasoning.

References in corpus (4)

• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
• Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction
• RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs
• DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks

Cited by in corpus (5)

• Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective
• Learning to Describe for Predicting Zero-shot Drug-Drug Interactions
• Knowledge-Enhanced Recommendation with User-Centric Subgraph Network
• Reevaluation of Inductive Link Prediction
• Towards Better Benchmark Datasets for Inductive Knowledge Graph Completion