@inproceedings{wei-etal-2025-survey,
    title = "A Survey of Link Prediction in N-ary Knowledge Graphs",
    author = "Wei, Jiyao  and
      Guan, Saiping  and
      Li, Da  and
      Hou, Zhongni  and
      Su, Miao  and
      Guo, Yucan  and
      Jin, Xiaolong  and
      Guo, Jiafeng  and
      Cheng, Xueqi",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1451/",
    pages = "28533--28555",
    ISBN = "979-8-89176-332-6",
    abstract = "N-ary Knowledge Graphs (NKGs) are a specialized type of knowledge graph designed to efficiently represent complex real-world facts. Unlike traditional knowledge graphs, where a fact typically involves two entities, NKGs can capture n-ary facts containing more than two entities. Link prediction in NKGs aims to predict missing elements within these n-ary facts, which is essential for completing NKGs and improving the performance of downstream applications. This task has recently gained significant attention. In this paper, we present the first comprehensive survey of link prediction in NKGs, providing an overview of the field, systematically categorizing existing methods, and analyzing their performance and application scenarios. We also outline promising directions for future research."
}