An Exact Approach to Learning Probabilistic Relational Model

Nourhene Ettouzi, Philippe Leray, Montassar Ben Messaoud
; Proceedings of the Eighth International Conference on Probabilistic Graphical Models, PMLR 52:171-182, 2016.

Abstract

Probabilistic Graphical Models (PGMs) offer a popular framework including a variety of statistical formalisms, such as Bayesian networks (BNs). These latter are able to depict real-world situations with high degree of uncertainty. Due to their power and flexibility, several extensions were proposed, ensuring thereby the suitability of their use. Probabilistic Relational Models (PRMs) extend BNs to work with relational databases rather than propositional data. Their construction represents an active area since it remains the most complicated issue. Only few works have been proposed in this direction, and most of them don’t guarantee an optimal identification of their dependency structure. In this paper we intend to propose an approach that ensures returning an optimal PRM structure. It is inspired from a BN method whose performance was already proven.

Cite this Paper


BibTeX
@InProceedings{pmlr-v52-ettouzi16, title = {An Exact Approach to Learning Probabilistic Relational Model}, author = {Nourhene Ettouzi and Philippe Leray and Montassar Ben Messaoud}, booktitle = {Proceedings of the Eighth International Conference on Probabilistic Graphical Models}, pages = {171--182}, year = {2016}, editor = {Alessandro Antonucci and Giorgio Corani and Cassio Polpo Campos}}, volume = {52}, series = {Proceedings of Machine Learning Research}, address = {Lugano, Switzerland}, month = {06--09 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v52/ettouzi16.pdf}, url = {http://proceedings.mlr.press/v52/ettouzi16.html}, abstract = {Probabilistic Graphical Models (PGMs) offer a popular framework including a variety of statistical formalisms, such as Bayesian networks (BNs). These latter are able to depict real-world situations with high degree of uncertainty. Due to their power and flexibility, several extensions were proposed, ensuring thereby the suitability of their use. Probabilistic Relational Models (PRMs) extend BNs to work with relational databases rather than propositional data. Their construction represents an active area since it remains the most complicated issue. Only few works have been proposed in this direction, and most of them don’t guarantee an optimal identification of their dependency structure. In this paper we intend to propose an approach that ensures returning an optimal PRM structure. It is inspired from a BN method whose performance was already proven.} }
Endnote
%0 Conference Paper %T An Exact Approach to Learning Probabilistic Relational Model %A Nourhene Ettouzi %A Philippe Leray %A Montassar Ben Messaoud %B Proceedings of the Eighth International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2016 %E Alessandro Antonucci %E Giorgio Corani %E Cassio Polpo Campos} %F pmlr-v52-ettouzi16 %I PMLR %J Proceedings of Machine Learning Research %P 171--182 %U http://proceedings.mlr.press %V 52 %W PMLR %X Probabilistic Graphical Models (PGMs) offer a popular framework including a variety of statistical formalisms, such as Bayesian networks (BNs). These latter are able to depict real-world situations with high degree of uncertainty. Due to their power and flexibility, several extensions were proposed, ensuring thereby the suitability of their use. Probabilistic Relational Models (PRMs) extend BNs to work with relational databases rather than propositional data. Their construction represents an active area since it remains the most complicated issue. Only few works have been proposed in this direction, and most of them don’t guarantee an optimal identification of their dependency structure. In this paper we intend to propose an approach that ensures returning an optimal PRM structure. It is inspired from a BN method whose performance was already proven.
RIS
TY - CPAPER TI - An Exact Approach to Learning Probabilistic Relational Model AU - Nourhene Ettouzi AU - Philippe Leray AU - Montassar Ben Messaoud BT - Proceedings of the Eighth International Conference on Probabilistic Graphical Models PY - 2016/08/15 DA - 2016/08/15 ED - Alessandro Antonucci ED - Giorgio Corani ED - Cassio Polpo Campos} ID - pmlr-v52-ettouzi16 PB - PMLR SP - 171 DP - PMLR EP - 182 L1 - http://proceedings.mlr.press/v52/ettouzi16.pdf UR - http://proceedings.mlr.press/v52/ettouzi16.html AB - Probabilistic Graphical Models (PGMs) offer a popular framework including a variety of statistical formalisms, such as Bayesian networks (BNs). These latter are able to depict real-world situations with high degree of uncertainty. Due to their power and flexibility, several extensions were proposed, ensuring thereby the suitability of their use. Probabilistic Relational Models (PRMs) extend BNs to work with relational databases rather than propositional data. Their construction represents an active area since it remains the most complicated issue. Only few works have been proposed in this direction, and most of them don’t guarantee an optimal identification of their dependency structure. In this paper we intend to propose an approach that ensures returning an optimal PRM structure. It is inspired from a BN method whose performance was already proven. ER -
APA
Ettouzi, N., Leray, P. & Ben Messaoud, M.. (2016). An Exact Approach to Learning Probabilistic Relational Model. Proceedings of the Eighth International Conference on Probabilistic Graphical Models, in PMLR 52:171-182

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