NDLI: Approximate Truth Discovery via Problem Scale Reduction

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Approximate Truth Discovery via Problem Scale Reduction

Content Provider	ACM Digital Library
Author	Sheng, Quan Z. Wang, Xianzhi Fang, Xiu Susie Li, Xue Xu, Xiaofei Yao, Lina
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 each 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 user-specified 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.
Starting Page	503
Ending Page	512
Page Count	10
File Format	PDF
ISBN	9781450337946
DOI	10.1145/2806416.2806444
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-10-17
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Consistency assurance Recursive method Problem scale reduction Truth discovery
Content Type	Text
Resource Type	Article

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