Sequential and Repetitive Pattern Learning for Temporal Knowledge Graph Reasoning

Abstract

Temporal Knowledge Graph (TKG) reasoning has received a growing interest recently, especially in forecasting the future facts based on the historical KG sequences. Existing studies typically utilize a recurrent neural network to learn the evolutional representations of entities for temporal reasoning. However, these methods are hard to capture the complex temporal evolutional patterns such as sequential and repetitive patterns accurately. To tackle this challenge, we propose a novel Sequential and Repetitive Pattern Learning (SRPL) method, which comprehensively captures both the sequential and repetitive patterns. Specifically, a Dependency-aware Sequential Pattern Learning (DSPL) component expresses the temporal dependencies of each historical timestamp as embeddings for accurately capturing the sequential patterns across temporally adjacent facts. A Time-interval guided Repetitive Pattern Learning (TRPL) component models the irregular time intervals between historical repetitive facts for capturing the repetitive patterns. Extensive experiments on four representative benchmarks demonstrate that our proposed method outperforms state-of-the-art methods in all metrics by an obvious margin, especially on GDELT dataset, where performance improvement of MRR reaches up to 18.84%.

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