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SemCaDo: A Serendipitous Strategy for Learning Causal Bayesian Networks Using Ontologies

  • Montassar Ben Messaoud
  • Philippe Leray
  • Nahla Ben Amor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6717)

Abstract

Learning Causal Bayesian Networks (CBNs) is a new line of research in the machine learning field. Within the existing works in this direction [8, 12, 13], few of them have taken into account the gain that can be expected when integrating additional knowledge during the learning process. In this paper, we present a new serendipitous strategy for learning CBNs using prior knowledge extracted from ontologies. The integration of such domain’s semantic information can be very useful to reveal new causal relations and provide the necessary knowledge to anticipate the optimal choice of experimentations. Our strategy also supports the evolving character of the semantic background by reusing the causal discoveries in order to enrich the domain ontologies.

Keywords

Causal Relation Domain Ontology Semantic Distance Ontology Evolution Edge Orientation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ben Messaoud, M., Leray, P., Ben Amor, N.: Integrating ontological knowledge for iterative causal discovery and visualization. In: Sossai, C., Chemello, G. (eds.) ECSQARU 2009. LNCS, vol. 5590, pp. 168–179. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Chickering, D.M.: Learning equivalence classes of bayesian-network structures. Journal of Machine Learning Research 2, 445–498 (2002)MathSciNetzbMATHGoogle Scholar
  3. 3.
    de Campos, L.M., Castellano, J.G.: Bayesian network learning algorithms using structural restrictions. International Journal of Approximate Reasoning, 233–254 (2007)Google Scholar
  4. 4.
    Devitt, A., Danev, B., Matusikova, K.: Constructing bayesian networks automatically using ontologies. In: Second Workshop on Formal Ontologies Meet Industry, FOMI 2006, Trento, Italy (2006)Google Scholar
  5. 5.
    Ding, Z., Peng, Y.: A probabilistic extension to ontology language OWL. In: Proceedings of the 37th Hawaii International Conference on System Sciences, HICSS 2004 (2004)Google Scholar
  6. 6.
    Flouris, G., Manakanatas, D., Kondylakis, H., Plexousakis, D., Antoniou, G.: Ontology change: classification and survey. The Knowledge Engineering Review 23, 117–152 (2008)CrossRefGoogle Scholar
  7. 7.
    Gruber, T.R.: Towards Principles for the Design of Ontologies Used for Knowledge Sharing. International Journal Human-Computer Studies 43(5-6), 907–928 (1995)CrossRefGoogle Scholar
  8. 8.
    He, Y.B., Geng, Z.: Active learning of causal networks with intervention experiments and optimal designs. JMLR 9, 2523–2547 (2008)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Jeon, B.-J., Ko, I.-Y.: Ontology-based semi-automatic construction of bayesian network models for diagnosing diseases in e-health applications. In: FBIT, pp. 595–602. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  10. 10.
    Khattak, A.M., Latif, K., Lee, S., Lee, Y.-K.: Ontology Evolution: A Survey and Future Challenges. In: Ślzak, D., Kim, T.-h., Ma, J., Fang, W.-C., Sandnes, F.E., Kang, B.-H., Gu, B. (eds.) U- and E-Service, Science and Technology, vol. 62, pp. 68–75. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Meek, C.: Causal inference and causal explanation with background knowledge. In: Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI 1995), pp. 403–441. Morgan Kaufmann, San Francisco (1995)Google Scholar
  12. 12.
    Meganck, S., Leray, P., Manderick, B.: Learning causal bayesian networks from observations and experiments: A decision theoretic approach. In: Torra, V., Narukawa, Y., Valls, A., Domingo-Ferrer, J. (eds.) MDAI 2006. LNCS (LNAI), vol. 3885, pp. 58–69. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Murphy, K.P.: Active learning of causal bayes net structure. Technical report, University of California, Berkeley, USA (2001)Google Scholar
  14. 14.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco (1988)zbMATHGoogle Scholar
  15. 15.
    Pearl, J.: Causality: models, reasoning, and inference. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  16. 16.
    Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics 19(1), 17–30 (1989)CrossRefGoogle Scholar
  17. 17.
    Xuan, D.N., Bellatreche, L., Pierra, G.: A versioning management model for ontology-based data warehouses. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. LNCS, vol. 4081, pp. 195–206. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Yang, Y., Calmet, J.: Ontobayes: An ontology-driven uncertainty model. In: International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA), pp. 457–463 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Montassar Ben Messaoud
    • 1
    • 2
  • Philippe Leray
    • 2
  • Nahla Ben Amor
    • 1
  1. 1.LARODEC, Institut Supérieur de Gestion TunisLe BardoFrance
  2. 2.Knowledge and Decision Team Laboratoire d’Informatique de Nantes Atlantique (LINA) UMR 6241Ecole Polytechnique de l’Université de NantesFrance

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