An integrated Bayesian approach for effective multi-truth discovery

Wang, Xianzhi, Sheng, Quan Z., Fang, Xiu Susie, Yao, Lina, Xu, Xiaofei and Li, Xue (2015). An integrated Bayesian approach for effective multi-truth discovery. In: CIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management. 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourne, Australia, (493-502). 19-23 October 2015. doi:10.1145/2806416.2806443


Author Wang, Xianzhi
Sheng, Quan Z.
Fang, Xiu Susie
Yao, Lina
Xu, Xiaofei
Li, Xue
Title of paper An integrated Bayesian approach for effective multi-truth discovery
Conference name 24th ACM International Conference on Information and Knowledge Management, CIKM 2015
Conference location Melbourne, Australia
Conference dates 19-23 October 2015
Convener Bailey, James
Proceedings title CIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management
Journal name International Conference on Information and Knowledge Management, Proceedings
Series International Conference on Information and Knowledge Management, Proceedings
Place of Publication New York, NY United States
Publisher The Association for Computing Machinery
Publication Year 2015
Year available 2015
Sub-type Fully published paper
DOI 10.1145/2806416.2806443
Open Access Status Not Open Access
ISBN 9781450337946
Volume 19-23-Oct-2015
Start page 493
End page 502
Total pages 10
Chapter number 51
Total chapters 244
Collection year 2016
Language eng
Formatted Abstract/Summary
Truth-finding is the fundamental technique for corroborating reports from multiple sources in both data integration and collective intelligent applications. Traditional truth-finding 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 truth-finding 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-truth-finding 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.
Keyword Model
Algorithms
Experimentation
Measurement
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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