Truth finding by reliability estimation on inconsistent entities for heterogeneous data sets

Abstract An important task in big data integration is to derive accurate data records from noisy and conflicting values collected from multiple sources. Most existing truth finding methods assume that the reliability is consistent on the whole data set, ignoring the fact that different attributes, objects and object groups may have different reliabilities even wrt the same source. These reliability differences are caused by the hardness differences in obtaining attribute values, non-uniform updates to objects and the differences in group privileges. This paper addresses the problem how to compute truths by effectively estimating the reliabilities of attributes, objects and object groups in a multi-source heterogeneous data environment. We first propose an optimization framework TFAR, its implementation and Lagrangian duality solution for Truth Finding by Attribute Reliability estimation. We then present a Bayesian probabilistic graphical model TFOR and an inference algorithm applying Collapsed Gibbs Sampling for Truth Finding by Object Reliability estimation. Finally we give an optimization framework TFGR and its implementation for Truth Finding by Group Reliability estimation. All these models lead to a more accurate estimation of the respective attribute, object and object group reliabilities, which in turn can achieve a better accuracy in inferring the truths. Experimental results on both real data and synthetic data show that our methods have better performance than the state-of-art truth discovery methods.