Self-learning and embedding based entity alignment

doi:10.1007/s10115-018-1191-0

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Self-learning and embedding based entity alignment
	Cheng, Xueqi 1,2; Guan, Saiping 1,2; Jin, Xiaolong 1,2; Wang, Yuanzhuo 1,2; Jia, Yantao 1,2; Shen, Huawei 1,2; Li, Zixuan 1,2
刊名	KNOWLEDGE AND INFORMATION SYSTEMS
	2019-05-01
卷号	59 期号:2 页码:361-386
关键词	Entity alignment Knowledge graph Self-learning Embedding
ISSN号	0219-1377
DOI	10.1007/s10115-018-1191-0
英文摘要	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.
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
语种	英语
出版者	SPRINGER LONDON LTD
WOS记录号	WOS:000461572500005
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/4143]
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.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Cheng, Xueqi,Guan, Saiping,Jin, Xiaolong,et al. Self-learning and embedding based entity alignment[J]. KNOWLEDGE AND INFORMATION SYSTEMS,2019,59(2):361-386.
APA	Cheng, Xueqi.,Guan, Saiping.,Jin, Xiaolong.,Wang, Yuanzhuo.,Jia, Yantao.,...&Li, Zixuan.(2019).Self-learning and embedding based entity alignment.KNOWLEDGE AND INFORMATION SYSTEMS,59(2),361-386.
MLA	Cheng, Xueqi,et al."Self-learning and embedding based entity alignment".KNOWLEDGE AND INFORMATION SYSTEMS 59.2(2019):361-386.