@inproceedings{li-etal-2024-sequential,
    title = "Sequential and Repetitive Pattern Learning for Temporal Knowledge Graph Reasoning",
    author = "Li, Xuefei  and
      Zhou, Huiwei  and
      Yao, Weihong  and
      Li, Wenchu  and
      Lin, Yingyu  and
      Du, Lei",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1284",
    pages = "14744--14754",
    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{\%}.",
}

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{\%}.
Li, Xuefei Zhou, Huiwei Yao, Weihong Li, Wenchu Lin, Yingyu Du, Lei
Sequential and Repetitive Pattern Learning for Temporal Knowledge Graph Reasoning