Paper Info
Title
An Integrated Bayesian Approach for Effective Multi-Truth Discovery
Abstract
Truth-finding is the fundamental technique for corroborating reports from multiple sources in both data integration and collective intelligent applications. Traditional truth-finding methods assume a single true value for each data item and therefore cannot deal will multiple true values (i.e., the multi-truth-finding problem). So far, the existing approaches handle the multi-truth-finding problem in the same way as the single-truth-finding problems. Unfortunately, the multi-truth-finding problem has its unique features, such as the involvement of sets of values in claims, different implications of inter-value mutual exclusion, and larger source profiles. Considering these features could provide new opportunities for obtaining more accurate truth-finding results. Based on this insight, we propose an integrated Bayesian approach to the multi-truth-finding problem, by taking these features into account. To improve the truth-finding efficiency, we reformulate the multi-truth-finding problem model based on the mappings between sources and (sets of) values. New mutual exclusive relations are defined to reflect the possible co-existence of multiple true values. A finer-grained copy detection method is also proposed to deal with sources with large profiles. The experimental results on three real-world datasets show the effectiveness of our approach.
Year
DOI
Venue
2015
10.1145/2806416.2806443
ACM International Conference on Information and Knowledge Management
Field
DocType
Citations 
Data integration,Data mining,Bayesian inference,Copy detection,Computer science,Artificial intelligence,Mutual exclusion,Machine learning,Bayesian probability
Conference
15
PageRank 
References 
Authors
0.71
16
6
Name
Order
Citations
PageRank
Xianzhi Wang127640.32
Quan Z. Sheng23520301.77
Xiu Susie Fang3455.56
Lina Yao498193.63
Xiaofei Xu540870.26
Xue Li62196186.96