Subgraph Reasoning on Temporal Knowledge Graphs for Forecasting Based on Relaxed Temporal Relations

Reasoning over Temporal Knowledge Graphs (TKGs) aims to forecast future events based on historical ones. Existing approaches typically enforce strict temporal order constraints among past events; however, such rigidity limits the effective exploitation of path information during reasoning, thereby reducing both model flexibility and predictive performance. To address this limitation, this paper introduces SR-RTR (Sub-graph Reasoning based on Relaxed Temporal Relation), an interpretable subgraph reasoning framework designed to fully harness path information within TKGs. By incorporating a relaxed temporal factor, the proposed method softens the chronological constraints on historical events, broadens the sampling scope of candidate nodes during subgraph reasoning, and enhances the efficiency of path information utilization. This mechanism reflects human cognitive intuition: when the temporal gap between two events falls within a certain threshold, their sequence can be considered interchangeable. SR-RTR constructs a query-specific inference subgraph from the TKG and iteratively performs two core operations—subgraph expansion and pruning—until the entity with the highest attention score is identified as the prediction result. Extensive experiments on four benchmark datasets demonstrate that SR-RTR uncovers a greater number of reasoning paths relevant to the target prediction, leading to substantial improvements in both reasoning accuracy and computational efficiency.