A new truth discovery method for resolving object conflicts over Linked Data with scale-free property

Considerable effort has been exerted to increase the scale of Linked Data. However, an inevitable problem arises when dealing with data integration from multiple sources. Various sources often provide conflicting objects for a certain predicate of the same real-world entity, thereby causing the so-called object conflict problem. Existing truth discovery methods cannot be trivially extended to resolve object conflict problems because Linked Data has a scale-free property, i.e., most of the sources provide few objects, whereas only a few sources have numerous objects. In this study, we propose a novel approach called TruthDiscover to determine the most trustworthy object in Linked Data with a scale-free property. More specifically, TruthDiscover consists of two core components: Priori Belief Estimation for smoothing the trustworthiness of sources by leveraging the topological properties of the Source Belief Graph, and Truth Computation for inferencing the trustworthiness of source and trust value of an object. Experimental results conducted on six datasets show that TruthDiscover achieves higher accuracy than existing approaches, and it is robust and consistent in various domains.