@inproceedings{zhang-etal-2024-bring,
    title = "Bring Invariant to Variant: A Contrastive Prompt-based Framework for Temporal Knowledge Graph Forecasting",
    author = "Zhang, Ying  and
      Qian, Xinying  and
      Zhao, Yu  and
      Zhou, Baohang  and
      Song, Kehui  and
      Yuan, Xiaojie",
    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.228",
    pages = "2526--2536",
    abstract = "Temporal knowledge graph forecasting aims to reason over known facts to complete the missing links in the future. Existing methods are highly dependent on the structures of temporal knowledge graphs and commonly utilize recurrent or graph neural networks for forecasting. However, entities that are infrequently observed or have not been seen recently face challenges in learning effective knowledge representations due to insufficient structural contexts. To address the above disadvantages, in this paper, we propose a Contrastive Prompt-based framework with Entity background information for TKG forecasting, which we named CoPET. Specifically, to bring the time-invariant entity background information to time-variant structural information, we employ a dual encoder architecture consisting of a candidate encoder and a query encoder. A contrastive learning framework is used to encourage the query representation to be closer to the candidate representation. We further propose three kinds of trainable time-variant prompts aimed at capturing temporal structural information. Experiments on two datasets demonstrate that our method is effective and stays competitive in inference with limited structural information. Our code is available at https://github.com/qianxinying/CoPET.",
}

Temporal knowledge graph forecasting aims to reason over known facts to complete the missing links in the future. Existing methods are highly dependent on the structures of temporal knowledge graphs and commonly utilize recurrent or graph neural networks for forecasting. However, entities that are infrequently observed or have not been seen recently face challenges in learning effective knowledge representations due to insufficient structural contexts. To address the above disadvantages, in this paper, we propose a Contrastive Prompt-based framework with Entity background information for TKG forecasting, which we named CoPET. Specifically, to bring the time-invariant entity background information to time-variant structural information, we employ a dual encoder architecture consisting of a candidate encoder and a query encoder. A contrastive learning framework is used to encourage the query representation to be closer to the candidate representation. We further propose three kinds of trainable time-variant prompts aimed at capturing temporal structural information. Experiments on two datasets demonstrate that our method is effective and stays competitive in inference with limited structural information. Our code is available at https://github.com/qianxinying/CoPET.
Zhang, Ying Qian, Xinying Zhao, Yu Zhou, Baohang Song, Kehui Yuan, Xiaojie
Bring Invariant to Variant: A Contrastive Prompt-based Framework for Temporal Knowledge Graph Forecasting