Abstract
Modeling cheese fabrication process helps experts to check their assumption on the domain such as finding which parameters (denoted as control parameters) can explain the final products and its properties. This modeling is however complex as it involves various parameters and a reasoning over different steps. Our previous work presents a method to learn a probabilistic relational model in order to check a user’s (an expert on the considered domain) assumption on a transformation process domain, using a knowledge base of this domain and his expert knowledge. However this method did not include temporal information, and thus the learned model is not enough to reason on the cheese fabrication process. In this article we present an extension of our previous work that allows a user to integrate causal and temporal information represented by precedence constraints in order to model a cheese fabrication process. This allows the user to check his assumption to identify the transformation process control parameters.
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.
- 2.
- 3.
- 4.
For convenience and in order to ease the readability of the presentation we use in this article a top-down construction (from temporality to causality). However nothing prevents us to use the opposite bottom-up construction (from causality to temporality).
References
Cooper, G.F., Herskovits, E.: A bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9(4), 309–347 (1992)
de Campos, C.P., Zeng, Z., Ji, Q.: Structure learning of bayesian networks using constraints. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, pp. 113–120. ACM, New York, NY, USA (2009)
Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI 1999, Stockholm, Sweden, July 31 - August 6, 1999. 2 Volumes, 1450 pages, pp. 1300–1309 (1999)
Liang, C., Forbus, K.D.: Learning plausible inferences from semantic web knowledge by combining analogical generalization with structured logistic regression. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI 2015, pp. 551–557. AAAI Press (2015)
Madigan, D., Andersson, S.A., Perlman, M.D., Volinsky, C.T.: Bayesian model averaging and model selection for markov equivalence classes of acyclic digraphs. Commun. Stat.-Theory Methods 25(11), 2493–2519 (1996)
Marini, S., et al.: A dynamic bayesian network model for long-term simulation of clinical complications in type 1 diabetes. J. Biomed. Inform. 57, 369–376 (2015)
Munch, M., Wuillemin, P.-H., Manfredotti, C.E., Dibie, J.: Towards interactive causal relation discovery driven by an ontology. Technical report (2018). https://hal.archives-ouvertes.fr/hal-01823862v1
Munch, M., Wuillemin, P.-H., Manfredotti, C.E., Dibie, J., Dervaux,S.: Learning probabilistic relational models using an ontology of transformation processes. In: On the Move to Meaningful Internet Systems. OTM 2017 Conferences - Confederated International Conferences: CoopIS, C&TC, and ODBASE 2017, Rhodes, Greece, 23–27 October 2017, Proceedings, Part II, pp. 198–2105 (2017)
O’Callaghan, T.F., et al.: Effect of pasture versus indoor feeding systems on quality characteristics, nutritional composition, and sensory and volatile properties of full-fat cheddar cheese. J. Dairy Sci. 100(8), 6053–6073 (2017)
de Campos, C.P., Ji, Q.: Improving bayesian network parameter learning using constraints, Jan 2009
Murphy, K.P.: Dynamic bayesian networks: representation, inference and learning, Jan 2002
Santiago-López, L., Aguilar-Toalá, J.E., Hernández-Mendoza, A., Vallejo-Cordoba, B., Liceaga, A.M., González-Córdova, A.F.: Invited review: bioactive compounds produced during cheese ripening and health effects associated with aged cheese consumption. J. Dairy Sci. 101(5), 3742–3757 (2018)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, 2nd edn. MIT press, Cambridge (2000)
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, Sept 2010
Wuillemin, P.-H., Torti, L.: Structured probabilistic inference. Int. J. Approx. Reason. 53(7), 946–968 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Munch, M. et al. (2018). Identifying Control Parameters in Cheese Fabrication Process Using Precedence Constraints. In: Soldatova, L., Vanschoren, J., Papadopoulos, G., Ceci, M. (eds) Discovery Science. DS 2018. Lecture Notes in Computer Science(), vol 11198. Springer, Cham. https://doi.org/10.1007/978-3-030-01771-2_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-01771-2_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01770-5
Online ISBN: 978-3-030-01771-2
eBook Packages: Computer ScienceComputer Science (R0)