LAUSR

Information gathered from multiple sources on the Web often exhibits conflicts. This phenomenon motivates the need of truth discovery, which aims to automatically find the true claim among multiple conflicting claims. Existing truth discovery methods are mainly based on iterative updates, optimization or probabilistic models. Although these methods have shown their own effectiveness, they have a common limitation. These methods do not model relationships between each pair of source and target such that they do not well capture the underlying interactions in the data. In this paper, we propose a new model for truth discovery, learning the representations of sources and claims automatically from the interactions between sources and targets. Our model first constructs a heterogenous network including source-claim, source-source and truth-claim relationships. It then embeds the network into a low dimensional space such that trustworthy sources and true claims are close. In this way, truth discovery can be conveniently performed in the embedding space. Moreover, our model can be implemented in both semi-supervised and un-supervised manners to deal with the label scarcity problem in practical truth discovery. Experiments on three real-world datasets demonstrate that our model outperforms existing state-of-the-art methods for truth discovery.