@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T A Survey of Link Prediction in N-ary Knowledge Graphs
%A Wei, Jiyao
%A Guan, Saiping
%A Li, Da
%A Hou, Zhongni
%A Su, Miao
%A Guo, Yucan
%A Jin, Xiaolong
%A Guo, Jiafeng
%A Cheng, Xueqi
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F wei-etal-2025-survey
%X 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.
%U https://aclanthology.org/2025.emnlp-main.1451/
%P 28533-28555
Markdown (Informal)
[A Survey of Link Prediction in N-ary Knowledge Graphs](https://aclanthology.org/2025.emnlp-main.1451/) (Wei et al., EMNLP 2025)
ACL
- Jiyao Wei, Saiping Guan, Da Li, Zhongni Hou, Miao Su, Yucan Guo, Xiaolong Jin, Jiafeng Guo, and Xueqi Cheng. 2025. A Survey of Link Prediction in N-ary Knowledge Graphs. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 28533–28555, Suzhou, China. Association for Computational Linguistics.