SourceVote: fusing multi-valued data via inter-source agreements

Xiu Susie Fang*, Quan Z. Sheng, Xianzhi Wang, Mahmoud Barhamgi, Lina Yao, Anne H. H. Ngu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)


Data fusion is a fundamental research problem of identifying true values of data items of interest from conflicting multi-sourced data. Although considerable research efforts have been conducted on this topic, existing approaches generally assume every data item has exactly one true value, which fails to reflect the real world where data items with multiple true values widely exist. In this paper, we propose a novel approach, SourceVote, to estimate value veracity for multi-valued data items. SourceVote models the endorsement relations among sources by quantifying their two-sided inter-source agreements. In particular, two graphs are constructed to model inter-source relations. Then two aspects of source reliability are derived from these graphs and are used for estimating value veracity and initializing existing data fusion methods. Empirical studies on two large real-world datasets demonstrate the effectiveness of our approach.

Original languageEnglish
Title of host publicationConceptual Modeling
Subtitle of host publication36th International Conference, ER 2017, Valencia, Spain, November 6–9, 2017, Proceedings
EditorsHeinrich C. Mayr, Giancarlo Guizzardi, Hui Ma, Oscar Pastor
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Number of pages9
ISBN (Electronic)9783319699042
ISBN (Print)9783319699035
Publication statusPublished - 2017
Event36th International Conference on Conceptual Modeling, ER 2017 - Valencia, Spain
Duration: 6 Nov 20179 Nov 2017

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameInformation Systems and Applications, incl. Internet/Web, and HCI


Conference36th International Conference on Conceptual Modeling, ER 2017


  • Data fusion
  • Data integration
  • Inter-source agreements
  • Multi-valued data items


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