Query-Aware Temporal Aggregation Network for Temporal Knowledge Graph Reasoning

Temporal Knowledge Graph (TKG) reasoning aims to predict facts at specific query timestamps using historical data. The main challenge is accurately modeling historical information for future queries. Previous approaches have primarily focused on global patterns and recent trends, but often overlooked query-relevant historical data. Although some recent methods have addressed this gap, they still struggle to capture implicit information and properly account for temporal dynamics. To overcome these limitations, we propose the Query-Aware Temporal Aggregation Network (QATAN), which adopts a novel “first evolution, then aggregation” strategy. QATAN effectively models the natural temporal evolution of facts and adaptively aggregates entity and relation embeddings based on query-relevant historical information through a query-aware temporal attention mechanism. Empirical evaluations demonstrate that QATAN outperforms state-of-the-art models on multiple benchmark datasets.