Matching Knowledge Graphs in Entity Embedding Spaces: An Experimental Study

doi:10.1109/TKDE.2023.3272584

	Matching Knowledge Graphs in Entity Embedding Spaces: An Experimental Study
	Zeng, Weixin 1; Zhao, Xiang 1; Tan, Zhen 2; Tang, Jiuyang 1; Cheng, Xueqi 3
	2023-12-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	35 期号:12 页码:12770-12784
摘要	Entity alignment (EA) identifies equivalent entities that locate in different knowledge graphs (KGs), and has attracted growing research interests over the last few years with the advancement of KG embedding techniques. Although a pile of embedding-based EA frameworks have been developed, they mainly focus on improving the performance of entity representation learning, while largely overlook the subsequent stage that matches KGs in entity embedding spaces. Nevertheless, accurately matching entities based on learned entity representations is crucial to the overall alignment performance, as it coordinates individual alignment decisions and determines the global matching result. Hence, it is essential to understand how well existing solutions for matching KGs in entity embedding spaces perform on present benchmarks, as well as their strengths and weaknesses. To this end, in this article we provide a comprehensive survey and evaluation of matching algorithms for KGs in entity embedding spaces in terms of effectiveness and efficiency on both classic settings and new scenarios that better mirror real-life challenges. Based on in-depth analysis, we provide useful insights into the design trade-offs and good paradigms of existing works, and suggest promising directions for future development.
关键词	Entity alignment entity matching knowledge graph knowledge graph alignment
DOI	10.1109/TKDE.2023.3272584
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2020AAA0108800] ; NSFC[62272469] ; NSFC[71971212]
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:001105152100016
出版者	IEEE COMPUTER SOC
引用统计	被引频次：5[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/2XEOYT63/38811
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhao, Xiang
作者单位	1.Natl Univ Def Technol, Lab Big Data & Decis, Changsha 410073, Hunan, Peoples R China 2.Natl Univ Def Technol, Sci & Technol Informat Syst Engn Lab, Changsha 410073, Hunan, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100045, Peoples R China
推荐引用方式 GB/T 7714	Zeng, Weixin,Zhao, Xiang,Tan, Zhen,et al. Matching Knowledge Graphs in Entity Embedding Spaces: An Experimental Study[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2023,35(12):12770-12784.
APA	Zeng, Weixin,Zhao, Xiang,Tan, Zhen,Tang, Jiuyang,&Cheng, Xueqi.(2023).Matching Knowledge Graphs in Entity Embedding Spaces: An Experimental Study.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,35(12),12770-12784.
MLA	Zeng, Weixin,et al."Matching Knowledge Graphs in Entity Embedding Spaces: An Experimental Study".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 35.12(2023):12770-12784.