TiRano: Tensorized Relation-aware Temporal Reasoning for Accurate Knowledge Graph Completion
Given a partially observed Temporal Knowledge Graph (TKG), how can we accurately predict missing entities? Unlike static knowledge graphs, TKGs encode facts within temporal contexts, requiring models to reason over both graph structure and time. However, existing TKGC approaches often sample neighbors solely based on temporal proximity, introducing irrelevant context and noise. Moreover, many methods compress snapshots into latent representations and rely on global sequence encoders for temporal modeling, losing edge-level structure and localized relation-specific patterns. In this paper, we propose TiRano (Tensorized Relation-aware temporal reasoning for knowledge graph completion), an accurate and efficient tensor-based temporal reasoning framework for TKGC. TiRano samples relation-adaptive temporal neighbors to construct compact, query-centric subgraphs, thereby reducing noise and computational overhead. Furthermore, TiRano organizes these subgraphs into structure-preserving, time-aligned snapshot tensors, and applies a relation-conditioned temporal convolution, which effectively captures localized edge-level temporal dynamics. Through extensive experiments, we demonstrate that TiRano consistently outperforms state-of-the-art TKGC methods in terms of both prediction accuracy and efficiency, achieving up to 12.3% higher accuracy and 2.4× faster inference.