Abstract
Probabilistic Relational Models (PRMs) extend Bayesian networks (BNs) with the notion of class of relational databases. Because of their richness, learning them is a difficult task. In this paper, we propose a method that learns a PRM from data using the semantic knowledge of an ontology describing these data in order to make the learning easier. To present our approach, we describe an implementation based on an ontology of transformation processes and compare its performance to that of a method that learns a PRM directly from data. We show that, even with small datasets, our approach of learning a PRM using an ontology is more efficient.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
These are two standard well known methods for learning BN. These and others methods can be found in [12].
References
Ben Ishak, M., Leray, P., Ben Amor, N.: Ontology-based generation of object oriented Bayesian networks. In: BMAW 2011, Spain, pp. 9–17 (2011). https://hal.archives-ouvertes.fr/hal-00644992
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)
Cutic, D., Gini, G.: Creating causal representations from ontologies and Bayesian networks. In: Arai, Gini, P.E. (eds.) Proceedings of Workshop NRF- IAS, Venice, Italy, pp. 1–12, 18–19th July 2014. http://home.deib.polimi.it/gini/papers/2014NFR.pdf
Ettouzi, N., Leray, P., Messaoud, M.B.: An exact approach to learning probabilistic relational model. In: Antonucci, A., Corani, G., Campos, C.P. (eds.) Proceedings of the Eighth International Conference on Probabilistic Graphical Models, pp. 171–182 (2016)
Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Dean, T. (ed.) Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI 1999, Stockholm, Sweden, 31 July–6 August 1999, vol. 2, pp. 1300–1309, 1450 pages. Morgan Kaufmann (1999). http://ijcai.org/Proceedings/99-2/Papers/090.pdf
Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning. Adaptive Computation and Machine Learning. The MIT Press, Cambridge (2007)
Gonzales, C., Torti, L., Wuillemin, P.H.: aGrUM: a graphical universal model framework. In: Proceedings of the 30th International Conference on Industrial Engineering, Other Applications of Applied Intelligent Systems, Arras, France, June 2017. https://hal.archives-ouvertes.fr/hal-01509651
Helsper, E.M., van der Gaag, L.C.: Building Bayesian networks through ontologies. In: Proceedings of the 15th Eureopean Conference on Artificial Intelligence, ECAI 2002, Lyon, France, July 2002, pp. 680–684 (2002)
Ibanescu, L., Dibie, J., Dervaux, S., Guichard, E., Raad, J.: PO\(^2\) - a process and observation ontology in food science. Application to dairy gels. In: Garoufallou, E., Subirats Coll, I., Stellato, A., Greenberg, J. (eds.) MTSR 2016. CCIS, vol. 672, pp. 155–165. Springer, Cham (2016). doi:10.1007/978-3-319-49157-8_13
Li, X.-L., Zhou, Z.-H.: Structure learning of probabilistic relational models from incomplete relational data. In: Kok, J.N., Koronacki, J., Mantaras, R.L., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS, vol. 4701, pp. 214–225. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74958-5_22
Manfredotti, C.E., Baudrit, C., Dibie-Barthélemy, J., Wuillemin, P.: Mapping ontology with probabilistic relational models. In: Fred, A.L.N., Dietz, J.L.G., Aveiro, D., Liu, K., Filipe, J. (eds.) KEOD 2015 - Proceedings of the International Conference on Knowledge Engineering and Ontology Development, part of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015), Lisbon, Portugal, 12–14 November 2015, vol. 2, pp. 171–178. SciTePress (2015). https://doi.org/10.5220/0005590001710178
Neapolitan, R.E.: Learning Bayesian Networks. Prentice-Hall Inc., Upper Saddle River (2003)
Staab, S., Studer, R. (eds.): Handbook on Ontologies. IHIS. Springer, Heidelberg (2009). doi:10.1007/978-3-540-92673-3
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. Intelligent Robotics and Autonomous Agents. The MIT Press, Cambridge (2005)
Torti, L., Wuillemin, P.H., Gonzales, C.: Reinforcing the object-oriented aspect of probabilistic relational models. In: PGM 2010 - The Fifth European Workshop on Probabilistic Graphical Models, Helsinki, Finland, pp. 273–280, September 2010. https://hal.archives-ouvertes.fr/hal-00627823
Wuillemin, P., Torti, L.: Structured probabilistic inference. Int. J. Approx. Reason. 53(7), 946–968 (2012). https://doi.org/10.1016/j.ijar.2012.04.004
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Munch, M., Wuillemin, PH., Manfredotti, C., Dibie, J., Dervaux, S. (2017). Learning Probabilistic Relational Models Using an Ontology of Transformation Processes. In: Panetto, H., et al. On the Move to Meaningful Internet Systems. OTM 2017 Conferences. OTM 2017. Lecture Notes in Computer Science(), vol 10574. Springer, Cham. https://doi.org/10.1007/978-3-319-69459-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-69459-7_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69458-0
Online ISBN: 978-3-319-69459-7
eBook Packages: Computer ScienceComputer Science (R0)