Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/110035
Citations
Scopus Web of Science® Altmetric
?
?
Type: Conference paper
Title: Empowering truth discovery with multi-truth prediction
Author: Wang, X.
Sheng, Q.
Yao, L.
Li, X.
Fang, X.
Xu, X.
Benatallah, B.
Citation: Proceedings of the 25th ACM International Conference on Information and Knowledge Management, 2016 / vol.24-28-October-2016, pp.881-890
Publisher: ACM
Issue Date: 2016
ISBN: 9781450340731
Conference Name: 25th ACM International Conference on Information and Knowledge Management (CIKM) (24 Oct 2016 - 28 Oct 2016 : Indianapolis, IN)
Statement of
Responsibility: 
Xianzhi Wang, Quan Z. Sheng, Lina Yao, Xue Li, Xiu Susie Fang, Xiaofei Xu, and Boualem Benatallah
Abstract: Truth discovery is the problem of detecting true values from the con icting data provided by multiple sources on the same data items. Since sources' reliability is unknown a priori, a truth discovery method usually estimates sources' reliability along with the truth discovery process. A major limitation of existing truth discovery methods is that they commonly assume exactly one true value on each data item and therefore cannot deal with the more general case that a data item may have multiple true values (or multi-truth). Since the number of true values may vary from data item to data item, this requires truth discovery methods being able to detect varying numbers of truth values from the multi source data. In this paper, we propose a multi-truth discovery approach, which addresses the above challenges by providing a generic framework for enhancing existing truth discovery methods. In particular, we redeem the numbers of true values as an important clue for facilitating multi-truth discovery. We present the procedure and components of our approach, and propose three models, namely the byproduct model, the joint model, and the synthesis model to implement our approach. We further propose two extensions to enhance our approach, by leveraging the implications of similar numerical values and values' co-occurrence informa- tion in sources' claims to improve the truth discovery accuracy. Experimental studies on real-world datasets demonstrate the effectiveness of our approach.
Keywords: Truth discovery; multiple truths; empowerment model; value co-occurrence
Rights: © 2016 ACM
RMID: 0030059271
DOI: 10.1145/2983323.2983767
Appears in Collections:Computer Science publications

Files in This Item:
File Description SizeFormat 
RA_hdl_110035.pdfRestricted Access1.16 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.