@inproceedings{liu-wang-2025-terdy,
    title = "{T}e{RD}y: Temporal Relation Dynamics through Frequency Decomposition for Temporal Knowledge Graph Completion",
    author = "Liu, Ziyang  and
      Wang, Chaokun",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.473/",
    pages = "9611--9622",
    ISBN = "979-8-89176-251-0",
    abstract = "Temporal knowledge graph completion aims to predict missing facts in a knowledge graph by leveraging temporal information. Existing methods often struggle to capture both the long-term changes and short-term variability of relations, which are crucial for accurate prediction. In this paper, we propose a novel method called TeRDy for temporal knowledge graph completion. TeRDy captures temporal relational dynamics by utilizing time-invariant embeddings, along with long-term temporally dynamic embeddings (e.g., enduring political alliances) and short-term temporally dynamic embeddings (e.g., transient political events). These two types of embeddings are derived from low- and high-frequency components via frequency decomposition. Also, we design temporal smoothing and temporal gradient to seamlessly incorporate timestamp embeddings into relation embeddings. Extensive experiments on benchmark datasets demonstrate that TeRDy outperforms state-of-the-art temporal knowledge graph embedding methods."
}