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Truth discovery has been widely studied in recent years as a fundamentalmeans for resolving the conflicts in multi-source data. Although many truthdiscovery methods have been proposed based on different considerations andintuitions, investigations show that no single method consistently outperformsthe others. To select the right truth discovery method for a specificapplication scenario, it becomes essential to evaluate and compare theperformance of different methods. A drawback of current research efforts isthat they commonly assume the availability of certain ground truth for theevaluation of methods. However, the ground truth may be very limited or evenout-of-reach in practice, rendering the evaluation biased by the small groundtruth or even unfeasible. In this paper, we present CompTruthHyp, a generalapproach for comparing the performance of truth discovery methods without usingground truth. In particular, our approach calculates the probability ofobservations in a dataset based on the output of different methods. Theprobability is then ranked to reflect the performance of these methods. Wereview and compare twelve existing truth discovery methods and consider bothsingle-valued and multi-valued objects. Empirical studies on both real-worldand synthetic datasets demonstrate the effectiveness of our approach forcomparing truth discovery methods.

From Appearance to Essence: Comparing Truth Discovery Methods without Using Ground Truth