TruthDiscover: Resolving Object Conflicts on Massive Linked Data

TL;DR

TruthDiscover is a system designed to resolve conflicting information in massive Linked Data by modeling source trustworthiness and object interdependencies, demonstrating high accuracy on scale-free datasets.

Contribution

It introduces a novel approach combining Source Belief Graphs and Hidden Markov Random Fields to effectively resolve conflicts in large-scale Linked Data.

Findings

Achieves high accuracy on four scale-free datasets

Effectively models source trustworthiness and object dependencies

Visualizes conflict resolution process

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. Experiments results on four datasets show that TruthDiscover exhibits satisfactory accuracy when confronted with data having a scale-free property.