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Latent truth discovery, LTD for short, refers to the problem of aggregatingltiple claims from various sources in order to estimate the plausibility ofatements about entities. In the absence of a ground truth, this problem ishighly challenging, when some sources provide conflicting claims and others noclaims at all. In this work we provide an unsupervised stochastic inferenceprocedure on top of a model that combines restricted Boltzmann machines withfeed-forward neural networks to accurately infer the reliability of sources aswell as the plausibility of statements about entities. In comparison to priorwork our approach stands out (1) by allowing the incorporation of arbitraryfeatures about sources and claims, (2) by generalizing from reliability persource towards a reliability function, and thus (3) enabling the estimation ofsource reliability even for sources that have provided no or very few claims,(4) by building on efficient and scalable stochastic inference algorithms, and(5) by outperforming the state-of-the-art by a considerable margin.

Combining Restricted Boltzmann Machines with Neural Networks for Latent Truth Discovery