Truth Discovery by Claim and Source Embedding

doi:10.1109/TKDE.2019.2936189

	Truth Discovery by Claim and Source Embedding
	Lyu, Shanshan 1,2; Ouyang, Wentao 1; Wang, Yongqing 1; Shen, Huawei 1,2; Cheng, Xueqi 1,2
	2021-03-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	33 期号:3 页码:1264-1275
摘要	Information gathered from multiple sources on the Web often exhibits conflicts. This phenomenon motivates the need of truth discovery, which aims to automatically find the true claim among multiple conflicting claims. Existing truth discovery methods are mainly based on iterative updates, optimization or probabilistic models. Although these methods have shown their own effectiveness, they have a common limitation. These methods do not model relationships between each pair of source and target such that they do not well capture the underlying interactions in the data. In this paper, we propose a new model for truth discovery, learning the representations of sources and claims automatically from the interactions between sources and targets. Our model first constructs a heterogenous network including source-claim, source-source and truth-claim relationships. It then embeds the network into a low dimensional space such that trustworthy sources and true claims are close. In this way, truth discovery can be conveniently performed in the embedding space. Moreover, our model can be implemented in both semi-supervised and un-supervised manners to deal with the label scarcity problem in practical truth discovery. Experiments on three real-world datasets demonstrate that our model outperforms existing state-of-the-art methods for truth discovery.
关键词	Reliability Object oriented modeling Iterative methods Probabilistic logic Data science Computer aided software engineering Data models Truth discovery crowdsourcing representation learning
DOI	10.1109/TKDE.2019.2936189
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFB0803302] ; National Natural Science Foundation of China[61425016] ; National Natural Science Foundation of China[61802371] ; National Natural Science Foundation of China[61602439] ; National Natural Science Foundation of China[91746301] ; K.C. Wong Education Foundation ; Beijing Academy of Artificial Intelligence ; CCF-Tencent Open Research Fund
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:000615042700033
出版者	IEEE COMPUTER SOC
引用统计	被引频次：10[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/2XEOYT63/16242
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ouyang, Wentao; Wang, Yongqing
作者单位	1.Chinese Acad Sci, CAS Key Lab Network Data Sci & Technol, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Lyu, Shanshan,Ouyang, Wentao,Wang, Yongqing,et al. Truth Discovery by Claim and Source Embedding[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2021,33(3):1264-1275.
APA	Lyu, Shanshan,Ouyang, Wentao,Wang, Yongqing,Shen, Huawei,&Cheng, Xueqi.(2021).Truth Discovery by Claim and Source Embedding.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,33(3),1264-1275.
MLA	Lyu, Shanshan,et al."Truth Discovery by Claim and Source Embedding".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 33.3(2021):1264-1275.