Link Prediction on N-ary Relational Data Based on Relatedness Evaluation

doi:10.1109/TKDE.2021.3073483

	Link Prediction on N-ary Relational Data Based on Relatedness Evaluation
	Guan, Saiping 1,2; Jin, Xiaolong 1,2; Guo, Jiafeng 1,2; Wang, Yuanzhuo None 1,2; Cheng, Xueqi 1,2
	2023
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	35 期号:1 页码:672-685
摘要	With the overwhelming popularity of Knowledge Graphs (KGs), researchers have poured attention to link prediction to fill in missing facts for a long time. However, they mainly focus on link prediction on binary relational data, where facts are usually represented as triples in the form of (head entity, relation, tail entity). In practice, n-ary relational facts are also ubiquitous. When encountering such facts, existing studies usually decompose them into triples by introducing a multitude of auxiliary virtual entities and additional triples. These conversions result in the complexity of carrying out link prediction on n-ary relational data. It has even proven that they may cause loss of structure information. To overcome these problems, in this paper, we represent each n-ary relational fact as a set of its role and role-value pairs. We then propose a method called NaLP to conduct link prediction on n-ary relational data, which explicitly models the relatedness of all the role and role-value pairs in an n-ary relational fact. We further extend NaLP by introducing type constraints of roles and role-values without any external type-specific supervision, and proposing a more reasonable negative sampling mechanism. Experimental results validate the effectiveness and merits of the proposed methods.
关键词	Link prediction n-ary relational facts knowledge graph relatedness
DOI	10.1109/TKDE.2021.3073483
收录类别	SCI
语种	英语
资助项目	National KeyResearch and Development Program of China[2016YFB1000902] ; Beijing Academy of ArtificialIntelligence (BAAI)[BAAI2019ZD0306] ; Lenovo-CAS Joint Lab Youth Scientist Project ; National Natural Science Foundation of China[62002341] ; National Natural Science Foundation of China[U1911401] ; National Natural Science Foundation of China[61772501] ; National Natural Science Foundation of China[U1836206] ; National Natural Science Foundation of China[91646120] ; GFKJ Innovation Program
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000895445500049
出版者	IEEE COMPUTER SOC
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/20188
专题	中国科学院计算技术研究所期刊论文
通讯作者	Guan, Saiping
作者单位	1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100864, Peoples R China
推荐引用方式 GB/T 7714	Guan, Saiping,Jin, Xiaolong,Guo, Jiafeng,et al. Link Prediction on N-ary Relational Data Based on Relatedness Evaluation[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2023,35(1):672-685.
APA	Guan, Saiping,Jin, Xiaolong,Guo, Jiafeng,Wang, Yuanzhuo None,&Cheng, Xueqi.(2023).Link Prediction on N-ary Relational Data Based on Relatedness Evaluation.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,35(1),672-685.
MLA	Guan, Saiping,et al."Link Prediction on N-ary Relational Data Based on Relatedness Evaluation".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 35.1(2023):672-685.