Paper Info
Title
On the Discovery of Evolving Truth
Abstract
In the era of big data, information regarding the same objects can be collected from increasingly more sources. Unfortunately, there usually exist conflicts among the information coming from different sources. To tackle this challenge, truth discovery, i.e., to integrate multi-source noisy information by estimating the reliability of each source, has emerged as a hot topic. In many real world applications, however, the information may come sequentially, and as a consequence, the truth of objects as well as the reliability of sources may be dynamically evolving. Existing truth discovery methods, unfortunately, cannot handle such scenarios. To address this problem, we investigate the temporal relations among both object truths and source reliability, and propose an incremental truth discovery framework that can dynamically update object truths and source weights upon the arrival of new data. Theoretical analysis is provided to show that the proposed method is guaranteed to converge at a fast rate. The experiments on three real world applications and a set of synthetic data demonstrate the advantages of the proposed method over state-of-the-art truth discovery methods.
Year
DOI
Venue
2015
10.1145/2783258.2783277
ACM Knowledge Discovery and Data Mining
Keywords
Field
DocType
Dynamic Data,Source Reliability,Truth Discovery
Data science,Data mining,Computer science,Dynamic data,Synthetic data,Big data
Conference
Volume
Citations 
PageRank 
2015
45
1.10
References 
Authors
17
7
Name
Order
Citations
PageRank
yaliang li162950.87
Qi Li250720.38
Jing Gao32723131.05
lu su4111866.61
Bo Zhao596936.08
Wei Fan64205253.58
Jiawei Han7430853824.48