Generating Graph-Based Rules for Enhancing Logical Reasoning

Inductive Knowledge Graph Completion (KGC) poses challenges due to the absence of emerging entities during training. Current methods utilize Graph Neural Networks (GNNs) to learn and propagate entity representations, achieving notable performance. However, these approaches primarily focus on chain-based logical rules, limiting their ability to capture the rich semantics of knowledge graphs. To address this challenge, we propose to generate Graph-based Rules for Enhancing Logical Reasoning (GRELR), a novel framework that leverages graph-based rules for enhanced reasoning. GRELR formulates graph-based rules by extracting relevant subgraphs and fuses them to construct comprehensive relation representations. This approach, combined with subgraph reasoning, significantly improves inference capabilities and showcases the potential of graph-based rules in inductive KGC. To demonstrate the effectiveness of the GRELR framework, we conduct experiments on three benchmark datasets, and our approach achieves state-of-the-art performance.