Paper Info
Title
TruthDiscover: Resolving Object Conflicts on Massive Linked Data.
Abstract
Considerable effort has been made to increase the scale of Linked Data. However, because of the openness of the Semantic Web and the ease of extracting Linked Data from semi-structured sources (e.g., Wikipedia) and unstructured sources, many Linked Data sources often provide conflicting objects for a certain predicate of a real-world entity. Existing methods cannot be trivially extended to resolve conflicts in Linked Data because Linked Data has a scale-free property. In this demonstration, we present a novel system called TruthDiscover, to identify the truth in Linked Data with a scale-free property. First, TruthDiscover leverages the topological properties of the Source Belief Graph to estimate the priori beliefs of sources, which are utilized to smooth the trustworthiness of sources. Second, the Hidden Markov Random Field is utilized to model interdependencies among objects for estimating the trust values of objects accurately. TruthDiscover can visualize the process of resolving conflicts in Linked Data.
Year
DOI
Venue
2017
10.1145/3041021.3054722
WWW (Companion Volume)
Field
DocType
Citations 
Interdependence,Data mining,Graph,World Wide Web,Hidden Markov random field,Computer science,Trustworthiness,Linked data,Semantic Web,Openness to experience,Predicate (grammar)
Conference
1
PageRank 
References 
Authors
0.35
4
6
Name
Order
Citations
PageRank
Wenqiang Liu183.17
Jun Liu217825.96
Haimeng Duan362.12
Jian Zhang424.09
Yuzhong Qu572662.49
Bifan Wei610.35