@inproceedings{zhao-etal-2026-stk,
    title = "{STK}-Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation",
    author = "Zhao, Shuyuan  and
      Chen, Wei  and
      Zhang, Weijie  and
      Hou, Xinrui  and
      Shen, Junfeng  and
      Shi, Boyan  and
      Guo, Shengnan  and
      Lin, Youfang  and
      Wan, Huaiyu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.905/",
    pages = "19763--19778",
    ISBN = "979-8-89176-390-6",
    abstract = "Temporal Knowledge Graph (TKG) extrapolation aims to predict future events based on historical facts. Recent studies have attempted to enhance TKG extrapolation by integrating TKG{'}s evolving structural representations and textual event chains into Large Language Models (LLMs). Yet, two main challenges limit these approaches: (1) The loss of essential spatial-temporal information due to shallow alignment between TKG{'}s graph evolving structural representation and the LLM{'}s semantic space, and (2) the progressive dilution of the TKG{'}s evolving structural features during LLM fine-tuning. To address these challenges, we propose the Spatial-Temporal Knowledge Adapter (STK-Adapter), which flexibly integrates the evolving graph encoder and the LLM to facilitate TKG reasoning. In STK-Adapter, a Spatial-Temporal MoE is designed to capture spatial structures and temporal patterns inherent in TKGs. An Event-Aware MoE is employed to model intricate temporal semantics dependencies within event chains. In addition, a Cross-Modality Alignment MoE is proposed to facilitate deep cross-modality alignment by TKG-guided attention experts. Extensive experiments on benchmark datasets demonstrate that STK-Adapter significantly outperforms state-of-the-art methods and exhibits strong generalization capabilities in cross-dataset task. The code is available at https://github.com/Zhaoshuyuan0246/STK-Adapter."
}