  Truth discovery has been widely studied in recent years as a fundamental
means for resolving the conflicts in multi-source data. Although many truth
discovery methods have been proposed based on different considerations and
intuitions, investigations show that no single method consistently outperforms
the others. To select the right truth discovery method for a specific
application scenario, it becomes essential to evaluate and compare the
performance of different methods. A drawback of current research efforts is
that they commonly assume the availability of certain ground truth for the
evaluation of methods. However, the ground truth may be very limited or even
out-of-reach in practice, rendering the evaluation biased by the small ground
truth or even unfeasible. In this paper, we present CompTruthHyp, a general
approach for comparing the performance of truth discovery methods without using
ground truth. In particular, our approach calculates the probability of
observations in a dataset based on the output of different methods. The
probability is then ranked to reflect the performance of these methods. We
review and compare twelve existing truth discovery methods and consider both
single-valued and multi-valued objects. Empirical studies on both real-world
and synthetic datasets demonstrate the effectiveness of our approach for
comparing truth discovery methods.

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