@inproceedings{deng-etal-2025-multi,
    title = "A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs",
    author = "Deng, Yimin  and
      Wu, Yuxia  and
      Wang, Yejing  and
      Zhao, Guoshuai  and
      Zhu, Li  and
      Liu, Qidong  and
      Xu, Derong  and
      Fu, Zichuan  and
      Wu, Xian  and
      Zheng, Yefeng  and
      Zhao, Xiangyu  and
      Qian, Xueming",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.1056/",
    pages = "20553--20565",
    ISBN = "979-8-89176-256-5",
    abstract = "Temporal knowledge graph reasoning aims to predict future events with knowledge of existing facts and plays a key role in various downstream tasks. Previous methods focused on either graph structure learning or semantic reasoning, failing to integrate dual reasoning perspectives to handle different prediction scenarios. Moreover, they lack the capability to capture the inherent differences between historical and non-historical events, which limits their generalization across different temporal contexts. To this end, we propose a **M**ulti-**E**xpert **S**tructural-**S**emantic **H**ybrid (MESH) framework that employs three kinds of expert modules to integrate both structural and semantic information, guiding the reasoning process for different events. Extensive experiments on three datasets demonstrate the effectiveness of our approach."
}