@inproceedings{fu-etal-2022-tempcaps,
    title = "{T}emp{C}aps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion",
    author = "Fu, Guirong  and
      Meng, Zhao  and
      Han, Zhen  and
      Ding, Zifeng  and
      Ma, Yunpu  and
      Schubert, Matthias  and
      Tresp, Volker  and
      Wattenhofer, Roger",
    booktitle = "Proceedings of the Sixth Workshop on Structured Prediction for NLP",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.spnlp-1.3",
    doi = "10.18653/v1/2022.spnlp-1.3",
    pages = "22--31",
    abstract = "Temporal knowledge graphs store the dynamics of entities and relations during a time period. However, typical temporal knowledge graphs often suffer from incomplete dynamics with missing facts in real-world scenarios. Hence, modeling temporal knowledge graphs to complete the missing facts is important. In this paper, we tackle the temporal knowledge graph completion task by proposing \textbf{TempCaps}, which is a \textbf{Caps}ule network-based embedding model for \textbf{Temp}oral knowledge graph completion. TempCaps models temporal knowledge graphs by introducing a novel dynamic routing aggregator inspired by Capsule Networks. Specifically, TempCaps builds entity embeddings by dynamically routing retrieved temporal relation and neighbor information. Experimental results demonstrate that TempCaps reaches state-of-the-art performance for temporal knowledge graph completion. Additional analysis also shows that TempCaps is efficient.",
}