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Type: Conference paper
Title: Approximate truth discovery via problem scale reduction
Author: Wang, X.
Sheng, Q.
Fang, X.
Li, X.
Xu, X.
Yao, L.
Citation: Proceedings of the 24th ACM International Conference on Information and Knowledge Management, 2015 / vol.19-23-Oct-2015, pp.503-512
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, Xue Li, Xiaofei Xu, and Lina Yao
Abstract: Many real-world applications rely on multiple data sources to provide information on their interested items. Due to the noises and uncertainty in data, given a specific item, the information from different sources may conflict. To make reliable decisions based on these data, it is important to identify the trustworthy information by resolving these conflicts, i.e., the truth discovery problem. Current solutions to this problem detect the veracity of each value jointly with the reliability of each source for every data item. In this way, the efficiency of truth discovery is strictly confined by the problem scale, which in turn limits truth discovery algorithms from being applicable on a large scale. To address this issue, we propose an approximate truth discovery approach, which divides sources and values into groups according to a userspecified approximation criterion. The groups are then used for efficient inter-value influence computation to improve the accuracy. Our approach is applicable to most existing truth discovery algorithms. Experiments on real-world datasets show that our approach improves the efficiency compared to existing algorithms while achieving similar or even better accuracy. The scalability is further demonstrated by experiments on large synthetic datasets.
Keywords: Truth discovery; problem scale reduction; recursive method; consistency assurance
Rights: © 2015 ACM
RMID: 0030044923
DOI: 10.1145/2806416.2806444
Appears in Collections:Computer Science publications

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