  We address the problem of latent truth discovery, LTD for short, where the
goal is to discover the underlying true values of entity attributes in the
presence of noisy, conflicting or incomplete information. Despite a multitude
of algorithms to address the LTD problem that can be found in literature, only
little is known about their overall performance with respect to effectiveness
(in terms of truth discovery capabilities), efficiency and robustness. A
practical LTD approach should satisfy all these characteristics so that it can
be applied to heterogeneous datasets of varying quality and degrees of
cleanliness.
  We propose a novel algorithm for LTD that satisfies the above requirements.
The proposed model is based on Restricted Boltzmann Machines, thus coined
LTD-RBM. In extensive experiments on various heterogeneous and publicly
available datasets, LTD-RBM is superior to state-of-the-art LTD techniques in
terms of an overall consideration of effectiveness, efficiency and robustness.
