Temporal Knowledge Graph Reasoning via Multigranularity Knowledge Refinement
Fuwei Zhang , Fuzhen Zhuang , Zhao Zhang , Pengpeng Zhao
Temporal Knowledge Graph (TKG) reasoning plays a pivotal role in predicting emerging facts based on historical data. However, existing TKG reasoning methods typically aggregate all historical facts within a given time window indiscriminately, which often introduces outdated or irrelevant information. This information redundancy can significantly hinder the reasoning performance, especially as TKGs continue to grow in scale and complexity. Effectively filtering out irrelevant facts is thus essential for improving inference accuracy and efficiency. To address this critical challenge, we focus on how to refine the TKGs and propose a Temporal knowledge graph reasoning model via Multi-granularity Knowledge Refinement (T-MKR). Specifically, we propose a multi-granularity knowledge refinement approach to prune historical TKGs, which selectively removes irrelevant edges and unnecessary nodes at both the edge and node levels. The resulting refined subgraphs are used for representation learning. To effectively combine information from both refinements, we introduce a subgraph gating integration module. Additionally, we leverage contrastive learning for subgraph alignment to emphasize the relationships between the two refined subgraphs. Extensive experiments on six commonly used datasets demonstrate the superiority of T-MKR compared to many state-of-the-art baselines.
Temporal Knowledge Graph / Temporal Knowledge Graph Reasoning / Knowledge Refinement
Higher Education Press
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