Value veracity estimation for multi-truth objects via a graph-based approach

Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Anne H.H. Ngu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

4 Citations (Scopus)
2 Downloads (Pure)

Abstract

A fundamental issue with current truth discovery methods is that they generally assume only one true value for each object, while in reality objects may have multiple true values. We propose a graph-based approach, called SmartMTD, to relax this assumption in truth discovery. SmartMTD models and quantifies two types of source relations to estimate source reliability precisely and to detect malicious agreement among sources for multi-truth discovery. Two graphs are constructed based on the modeled source relations, which are further used to derive two aspects of source reliability via random walk computation.

Original languageEnglish
Title of host publication26th International World Wide Web Conference 2017, WWW 2017 Companion
PublisherInternational World Wide Web Conferences Steering Committee
Pages777-778
Number of pages2
ISBN (Electronic)9781450349147
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event26th International World Wide Web Conference, WWW 2017 Companion - Perth, Australia
Duration: 3 Apr 20177 Apr 2017

Conference

Conference26th International World Wide Web Conference, WWW 2017 Companion
CountryAustralia
CityPerth
Period3/04/177/04/17

Bibliographical note

Copyright 2017 International World Wide Web Conference Committee (IW3C2). Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Copy Detection
  • Multi-Truth Discovery
  • Object Popularity

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