@inproceedings{luo-etal-2025-gltw,
    title = "{GLTW}: Joint Improved Graph Transformer and {LLM} via Three-Word Language for Knowledge Graph Completion",
    author = "Luo, Kangyang  and
      Bai, Yuzhuo  and
      Gao, Cheng  and
      Si, Shuzheng  and
      Liu, Zhu  and
      Shen, Yingli  and
      Wang, Zhitong  and
      Kong, Cunliang  and
      Li, Wenhao  and
      Huang, Yufei  and
      Tian, Ye  and
      Xiong, Xuantang  and
      Han, Lei  and
      Sun, Maosong",
    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/landing_page/2025.findings-acl.591/",
    pages = "11328--11344",
    ISBN = "979-8-89176-256-5",
    abstract = "Knowledge Graph Completion (KGC), which aims to infer missing or incomplete facts, is a crucial task for KGs. However, integrating the vital structural information of KGs into Large Language Models (LLMs) and outputting predictions deterministically remains challenging. To address this, we propose a new method called \textbf{GLTW}, which encodes the structural information of KGs and merges it with LLMs to enhance KGC performance. Specifically, we introduce an improved Graph Transformer (\textbf{iGT}) that effectively encodes subgraphs with both local and global structural information and inherits the characteristics of language model, bypassing training from scratch. Also, we develop a subgraph-based multi-classification training objective, using all entities within KG as classification objects, to boost learning efficiency. Importantly, we combine iGT with an LLM that takes KG language prompts as input. Our extensive experiments on various KG datasets show that GLTW achieves significant performance gains compared to SOTA baselines."
}