Advertisement

Identifying Control Parameters in Cheese Fabrication Process Using Precedence Constraints

  • Melanie MunchEmail author
  • Pierre-Henri Wuillemin
  • Juliette Dibie
  • Cristina Manfredotti
  • Thomas Allard
  • Solange Buchin
  • Elisabeth Guichard
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11198)

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.

Keywords

Ontology Probabilistic relational model Temporality 

References

  1. 1.
    Cooper, G.F., Herskovits, E.: A bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9(4), 309–347 (1992)zbMATHGoogle Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    de Campos, C.P., Ji, Q.: Improving bayesian network parameter learning using constraints, Jan 2009Google Scholar
  11. 11.
    Murphy, K.P.: Dynamic bayesian networks: representation, inference and learning, Jan 2002Google Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, 2nd edn. MIT press, Cambridge (2000)zbMATHGoogle Scholar
  14. 14.
    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 2010Google Scholar
  15. 15.
    Wuillemin, P.-H., Torti, L.: Structured probabilistic inference. Int. J. Approx. Reason. 53(7), 946–968 (2012)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Melanie Munch
    • 1
    Email author
  • Pierre-Henri Wuillemin
    • 2
  • Juliette Dibie
    • 1
  • Cristina Manfredotti
    • 1
  • Thomas Allard
    • 3
  • Solange Buchin
    • 4
  • Elisabeth Guichard
    • 3
  1. 1.UMR MIA-Paris, AgroParisTech, INRAUniversité Paris-SaclayParisFrance
  2. 2.Sorbonne University, UPMC, Univ Paris 06, CNRS UMR 7606, LIP6ParisFrance
  3. 3.CSGA, AgroSupDijon, CNRS, INRA, Université Bourgogne Franche-ComtéDijonFrance
  4. 4.UR342 INRAPolignyFrance

Personalised recommendations