An End-to-End Deep Neural Network for Truth Discovery

With the rapid growth of web data, information about the same target gathered from multiple sources often exhibits conflicts. This problem motivates the need for truth discovery, which is to automatically resolve conflicts and find the truth from multiple conflicting claims. Existing truth discovery methods are mainly based on iterative updates or probability models. A common limitation of these methods is that their models are complex to be built. In this paper, we propose a concise end-to-end deep neural network for truth discovery, which regards the task as a classification problem. Firstly, for each target, we extract a unique claim, and for each unique claim, we construct a source-unique-claim vector depending on whether the source provides this value. Then on the training dataset, we label the vector as true/false according to the ground truth. Finally, we use a deep neural network to build a classification model for each target to judge which claim is the truth. Experimental results on two real-world datasets show that our proposed model has better performance than existing state-of-the-art methods.