Restricted Boltzmann Machines for Robust and Fast Latent Truth Discovery
This addresses the LTD problem for applications needing robust and fast truth discovery from noisy data, but it appears incremental as it builds on existing LTD methods with a new model.
The authors tackled the problem of latent truth discovery (LTD) by proposing LTD-RBM, a novel algorithm based on Restricted Boltzmann Machines, which outperformed state-of-the-art techniques in effectiveness, efficiency, and robustness across various heterogeneous datasets.
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.