Title
A confidence-aware approach for truth discovery on long-tail data
Abstract
In many real world applications, the same item may be described by multiple sources. As a consequence, conflicts among these sources are inevitable, which leads to an important task: how to identify which piece of information is trustworthy, i.e., the truth discovery task. Intuitively, if the piece of information is from a reliable source, then it is more trustworthy, and the source that provides trustworthy information is more reliable. Based on this principle, truth discovery approaches have been proposed to infer source reliability degrees and the most trustworthy information (i.e., the truth) simultaneously. However, existing approaches overlook the ubiquitous long-tail phenomenon in the tasks, i.e., most sources only provide a few claims and only a few sources make plenty of claims, which causes the source reliability estimation for small sources to be unreasonable. To tackle this challenge, we propose a confidence-aware truth discovery (CATD) method to automatically detect truths from conflicting data with long-tail phenomenon. The proposed method not only estimates source reliability, but also considers the confidence interval of the estimation, so that it can effectively reflect real source reliability for sources with various levels of participation. Experiments on four real world tasks as well as simulated multi-source long-tail datasets demonstrate that the proposed method outperforms existing state-of-the-art truth discovery approaches by successful discounting the effect of small sources.
Year
DOI
Venue
2014
10.14778/2735496.2735505
VLDB
Field
DocType
Volume
Data science,Data mining,Discounting,Computer science,Trustworthiness,Phenomenon,Database
Journal
8
Issue
ISSN
Citations 
4
2150-8097
89
PageRank 
References 
Authors
2.38
28
8
Name
Order
Citations
PageRank
Qi Li150720.38
yaliang li262950.87
Jing Gao32723131.05
lu su4111866.61
Bo Zhao596936.08
Murat Demirbas61670102.80
Wei Fan74205253.58
Jiawei Han8430853824.48