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Type: Conference paper
Title: An integrated bayesian approach for effective multi-truth discovery
Author: Wang, X.
Sheng, Q.
Fang, X.
Yao, L.
Xu, X.
Li, X.
Citation: Proceedings of the 24th ACM International Conference on Information and Knowledge Management, 2015 / vol.19-23-Oct-2015, pp.493-502
Publisher: ACM
Issue Date: 2015
ISBN: 9781450337946
Conference Name: 24th ACM International Conference on Information and Knowledge Management (CIKM) (19 Oct 2015 - 23 Oct 2015 : Melbourne, Vic)
Statement of
Xianzhi Wang, Quan Z. Sheng, Xiu Susie Fang, Lina Yao, Xiaofei Xu, Xue Li
Abstract: Truth-finding is the fundamental technique for corroborating reports from multiple sources in both data integration and collective intelligent applications. Traditional truthfinding 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 truthfinding 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-truthfinding 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.
Keywords: Truth discovery; multi-truth-finding features; Bayesian model; data source dependence
Rights: © 2015 ACM
RMID: 0030044922
DOI: 10.1145/2806416.2806443
Appears in Collections:Computer Science publications

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