LTD-RBM: Robust and fast latent truth discovery using restricted boltzmann machines

Klaus Broelemann; Thomas Gottron; Gjergji Kasneci

doi:10.1109/ICDE.2017.60

LTD-RBM: Robust and fast latent truth discovery using restricted boltzmann machines

Klaus Broelemann, Thomas Gottron, Gjergji Kasneci

SCHUFA Holding AG

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
Publisher	IEEE Computer Society
Pages	143-146
Number of pages	4
ISBN (Electronic)	9781509065431
DOIs	https://doi.org/10.1109/ICDE.2017.60
State	Published - 16 May 2017
Externally published	Yes
Event	33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States Duration: 19 Apr 2017 → 22 Apr 2017

Publication series

Name	Proceedings - International Conference on Data Engineering
ISSN (Print)	1084-4627

Conference

Conference	33rd IEEE International Conference on Data Engineering, ICDE 2017
Country/Territory	United States
City	San Diego
Period	19/04/17 → 22/04/17

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10.1109/ICDE.2017.60

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Broelemann, K., Gottron, T., & Kasneci, G. (2017). LTD-RBM: Robust and fast latent truth discovery using restricted boltzmann machines. In Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017 (pp. 143-146). Article 7929956 (Proceedings - International Conference on Data Engineering). IEEE Computer Society. https://doi.org/10.1109/ICDE.2017.60

@inproceedings{71b28558a4ee43538bd4b156068499be,

title = "LTD-RBM: Robust and fast latent truth discovery using restricted boltzmann machines",

abstract = "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.",

author = "Klaus Broelemann and Thomas Gottron and Gjergji Kasneci",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 33rd IEEE International Conference on Data Engineering, ICDE 2017 ; Conference date: 19-04-2017 Through 22-04-2017",

year = "2017",

month = may,

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doi = "10.1109/ICDE.2017.60",

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Broelemann, K, Gottron, T & Kasneci, G 2017, LTD-RBM: Robust and fast latent truth discovery using restricted boltzmann machines. in Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017., 7929956, Proceedings - International Conference on Data Engineering, IEEE Computer Society, pp. 143-146, 33rd IEEE International Conference on Data Engineering, ICDE 2017, San Diego, United States, 19/04/17. https://doi.org/10.1109/ICDE.2017.60

LTD-RBM: Robust and fast latent truth discovery using restricted boltzmann machines. / Broelemann, Klaus; Gottron, Thomas; Kasneci, Gjergji.
Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017. IEEE Computer Society, 2017. p. 143-146 7929956 (Proceedings - International Conference on Data Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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BT - Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017

PB - IEEE Computer Society

Y2 - 19 April 2017 through 22 April 2017

ER -

LTD-RBM: Robust and fast latent truth discovery using restricted boltzmann machines

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