LTD-RBM: Robust and Fast Latent Truth Discovery Using Restricted Boltzmann Machines

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 addressing the LTD problem, only little is known about the...

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Bibliographic Details
Main Authors: Broelemann, Klaus, Gottron, Thomas, Kasneci, Gjergji
Format: Conference Proceeding
Language:English
Subjects:
Data models
Inference algorithms
Maximum likelihood estimation
Probabilistic logic
Robustness
Training
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Summary: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 addressing the LTD problem, only little is known about their overall performance with respect to effectiveness, efficiency and robustness. The LTD model proposed in this paper 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.
ISSN:2375-026X
DOI:10.1109/ICDE.2017.60