Self-learning and embedding based entity alignment

doi:10.1007/s10115-018-1191-0

	Self-learning and embedding based entity alignment
	Guan, Saiping 1,2; Jin, Xiaolong 1,2; Wang, Yuanzhuo 1,2; Jia, Yantao 1,2; Shen, Huawei 1,2; Li, Zixuan 1,2; Cheng, Xueqi 1,2
	2019-05-01
发表期刊	KNOWLEDGE AND INFORMATION SYSTEMS
ISSN	0219-1377
卷号	59 期号:2 页码:361-386
摘要	Entity alignment aims to identify semantical matchings between entities from different groups. Traditional methods (e.g., attribute comparison-based methods, graph operation-based methods and active learning ones) are usually supervised by labeled data as prior knowledge. Since it is not trivial to label data for training, researchers have then turned to unsupervised methods, and have thus developed similarity-based methods, probabilistic methods, graphical model-based methods, etc. In addition, structure or class information is further explored. As an important part of a knowledge graph, entities contain rich semantical information that can be well learned by knowledge graph embedding methods in low-dimensional vector spaces. However, existing methods for entity alignment have paid little attention to knowledge graph embedding. In this paper, we propose a self-learning and embedding based method for entity alignment, thus called SEEA, to iteratively find semantically aligned entity pairs, which makes full use of semantical information contained in the attributes of entities. Experiments on three realistic datasets and comparison with a few baseline methods validate the effectiveness and merits of the proposed method.
关键词	Entity alignment Knowledge graph Self-learning Embedding
DOI	10.1007/s10115-018-1191-0
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2016YFB1000902] ; National Key Research and Development Program of China[2017YFC0820404] ; National Natural Science Foundation of China[61772501] ; National Natural Science Foundation of China[61572473] ; National Natural Science Foundation of China[61572469] ; National Natural Science Foundation of China[91646120]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS记录号	WOS:000461572500005
出版者	SPRINGER LONDON LTD
引用统计	被引频次：12[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/2XEOYT63/4144
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Jin, Xiaolong
作者单位	1.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Guan, Saiping,Jin, Xiaolong,Wang, Yuanzhuo,et al. Self-learning and embedding based entity alignment[J]. KNOWLEDGE AND INFORMATION SYSTEMS,2019,59(2):361-386.
APA	Guan, Saiping.,Jin, Xiaolong.,Wang, Yuanzhuo.,Jia, Yantao.,Shen, Huawei.,...&Cheng, Xueqi.(2019).Self-learning and embedding based entity alignment.KNOWLEDGE AND INFORMATION SYSTEMS,59(2),361-386.
MLA	Guan, Saiping,et al."Self-learning and embedding based entity alignment".KNOWLEDGE AND INFORMATION SYSTEMS 59.2(2019):361-386.