Abstract
Bayesian networks (BN) have been used for prediction or classification tasks in various domains. In the first applications, the BN structure was causally defined by expert knowledge. Then, algorithms were proposed in order to learn the BN structure from observational data. Generally, these algorithms can only find a structure encoding the right conditional independencies but not all the causal relationships. Some new domains appear where the model will only be learnt in order to discover these causal relationships. To this end, we will focus on discovering causal relations in order to get Causal Bayesian Networks (CBN). To learn such models, interventional data (i.e. samples conditioned on the particular values of one or more variables that have been experimentally manipulated) are required. These interventions are usually very expensive to perform, therefore the choice of variables to experiment on can be vital when the number of experimentations is restricted. In many cases, available ontologies provide high level knowledge for the same domain under study. Consequently, using this semantical knowledge can turn out of a big utility to improve causal discovery. This article proposes a new method for learning CBNs from observational data and interventions. We first extend the greedy approach for perfect observational and experimental data proposed in [13], by adding a new step based on the integration of ontological knowledge, which will allow us to choose efficiently the interventions to perform in order to obtain the complete CBN. Then, we propose an enriched visualization for better understanding of the causal graphs.
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References
Blanchard, E., Harzallah, M., Briand, H., Kuntz, P.: A typology of ontology-based semantic measures. In: 2nd INTEROP-EMOI Open Workshop on Enterprise Models and Ontologies for Interoperability at the 17th Conference on Advanced Information Systems Engineering (CAISE 2005), vol. 160, pp. 407–412. CEUR-WS (2005)
Borchani, H., Chaouachi, M., Ben Amor, N.: Learning causal bayesian networks from incomplete observational data and interventions. In: Proceedings of Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp. 17–29 (2007)
Cannataro, M., Massara, A., Veltri, P.: The OnBrowser ontology manager: Managing ontologies on the Grid. In: International Workshop on Semantic Intelligent Middleware for the Web and the Grid, Valencia, Spain (2004)
Chickering, D.M.: Optimal Structure Identification With Greedy Search. Journal of Machine Learning Research, 507–554 (2002)
Cooper, G.F., Herskovits, E.: A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning, 309–347 (1992)
Cooper, G.F., Yoo, C.: Causal discovery from a mixture of experimental and observational data. In: Proceedings of Uncertainty in Artificial Intelligence, pp. 116–125 (1999)
Crampes, M., Ranwez, S., Villerd, J., Velickovski, F., Mooney, C., Emery, A., Mille, N.: Concept Maps for Designing Adaptive Knowledge Maps. In: Tergan, S.-O., Keller, T., Burkhard, R. (Guest eds.) Concept Maps, A Special Issue of Information Visualization, vol. 5(3). Palgrave - Macmillan (2006)
Crampes, M., Ranwez, S., Velickovski, F., Mooney, C., Mille, N.: An integrated visual approach for music indexing and dynamic playlist composition. In: Proceedings of 13th Annual Multimedia Computing and Networking (MMCN 2006), San Jose, CA, US (2006)
Eberhardt, F., Glymour, C., Scheines, R.: N-1 experiments suffice to determine the causal relations among n variables. Technical report, Carnegie Mellon University (2005)
Gruber, T.R.: Toward Principles for the Design of Ontologies Used for Knowledge Sharing. International Journal Human-Computer Studies 43, 907–928 (1993)
Maes, S., Meganck, S., Leray, P.: An integral approach to causal inference with latent variables. In: Russo, F., Williamson, J. (eds.) Causality and Probability in the Sciences (Texts in Philosophy), pp. 17–41. College Publications (2007)
Mani, S., Cooper, G.F.: Causal discovery using a Bayesian local causal discovery algorithm. In: Proceedings of MedInfo, pp. 731–735. IOS Press, Amsterdam (2004)
Meganck, S., Leray, P., Manderick, B.: Learning causal bayesian networks from observations and experiments: A decision theoritic approach. In: Torra, V., Narukawa, Y., Valls, A., Domingo-Ferrer, J. (eds.) MDAI 2006. LNCS, vol. 3885, pp. 58–69. Springer, Heidelberg (2006)
Meganck, S., Maes, S., Leray, P., Manderick, B.: Learning semi-markovian causal models using experiments. In: Proceedings of The third European Workshop on Probabilistic Graphical Models, PGM 2006, pp. 195–206 (2006)
Meganck, S., Leray, P., Manderick, B.: Causal graphical models with latent variables: Learning and inference. In: Mellouli, K. (ed.) ECSQARU 2007. LNCS, vol. 4724, pp. 5–16. Springer, Heidelberg (2007)
Pan, R., Ding, Z., Yu, Y., Peng, Y.: A Bayesian Network Approach to Ontology Mapping. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 563–577. Springer, Heidelberg (2005)
Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers, San Francisco (1988)
Pearl, J.: Causality: Models, reasoning and inference. MIT Press, Cambridge (2000)
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, 17–30 (1989)
Ranwez, S., Ranwez, V., Villerd, J., Crampes, M.: Ontological Distance Measures for Information Visualization on Conceptual Maps. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2006 Workshops. LNCS, vol. 4278, pp. 1050–1061. Springer, Heidelberg (2006)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction and Search. MIT Press, Cambridge (2000)
Sváb, O., Svátek, V.: Combining Ontology Mapping Methods Using Bayesian Networks. In: Workshop on Ontology Matching at ISWC (2006)
Tong, S., Koller, D.: Active learning for structure in bayesian networks. In: Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI), pp. 863–869 (2001)
Verma, T., Pearl, J.: Equivalence and Synthesis of Causal Models. In: Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (1990)
The Gene Ontology Consortium. Gene Ontology: tool for the unification of biology, Nature Genet., 25–29 (2000)
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Ben Messaoud, M., Leray, P., Ben Amor, N. (2009). Integrating Ontological Knowledge for Iterative Causal Discovery and Visualization. In: Sossai, C., Chemello, G. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2009. Lecture Notes in Computer Science(), vol 5590. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02906-6_16
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DOI: https://doi.org/10.1007/978-3-642-02906-6_16
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