@inproceedings{han-etal-2023-ecola,
    title = "{ECOLA}: Enhancing Temporal Knowledge Embeddings with Contextualized Language Representations",
    author = {Han, Zhen  and
      Liao, Ruotong  and
      Gu, Jindong  and
      Zhang, Yao  and
      Ding, Zifeng  and
      Gu, Yujia  and
      Koeppl, Heinz  and
      Sch{\"u}tze, Hinrich  and
      Tresp, Volker},
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.335",
    doi = "10.18653/v1/2023.findings-acl.335",
    pages = "5433--5447",
    abstract = "Since conventional knowledge embedding models cannot take full advantage of the abundant textual information, there have been extensive research efforts in enhancing knowledge embedding using texts. However, existing enhancement approaches cannot apply to \textit{temporal knowledge graphs} (tKGs), which contain time-dependent event knowledge with complex temporal dynamics. Specifically, existing enhancement approaches often assume knowledge embedding is time-independent. In contrast, the entity embedding in tKG models usually evolves, which poses the challenge of aligning \textit{temporally relevant} texts with entities. To this end, we propose to study enhancing temporal knowledge embedding with textual data in this paper. As an approach to this task, we propose Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations (ECOLA), which takes the temporal aspect into account and injects textual information into temporal knowledge embedding. To evaluate ECOLA, we introduce three new datasets for training and evaluating ECOLA. Extensive experiments show that ECOLA significantly enhances temporal KG embedding models with up to 287{\%} relative improvements regarding Hits@1 on the link prediction task. The code and models are publicly available on \url{https://github.com/mayhugotong/ECOLA}.",
}

Since conventional knowledge embedding models cannot take full advantage of the abundant textual information, there have been extensive research efforts in enhancing knowledge embedding using texts. However, existing enhancement approaches cannot apply to \textit{temporal knowledge graphs} (tKGs), which contain time-dependent event knowledge with complex temporal dynamics. Specifically, existing enhancement approaches often assume knowledge embedding is time-independent. In contrast, the entity embedding in tKG models usually evolves, which poses the challenge of aligning \textit{temporally relevant} texts with entities. To this end, we propose to study enhancing temporal knowledge embedding with textual data in this paper. As an approach to this task, we propose Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations (ECOLA), which takes the temporal aspect into account and injects textual information into temporal knowledge embedding. To evaluate ECOLA, we introduce three new datasets for training and evaluating ECOLA. Extensive experiments show that ECOLA significantly enhances temporal KG embedding models with up to 287{\%} relative improvements regarding Hits@1 on the link prediction task. The code and models are publicly available on \url{https://github.com/mayhugotong/ECOLA}.
Han, Zhen Liao, Ruotong Gu, Jindong Zhang, Yao Ding, Zifeng Gu, Yujia Koeppl, Heinz Sch{\"u}tze, Hinrich Tresp, Volker
ECOLA: Enhancing Temporal Knowledge Embeddings with Contextualized Language Representations