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SemCaDo: une approche pour la découverte de connaissances fortuites et l'évolution ontologique

Abstract : With the rising need to reuse the existing domain knowledge when learning causal Bayesian networks, the ontologies can supply valuable semantic information to de ne explicit cause-to-e ect relationships and make further interesting discoveries with the minimum expected cost and e ort. This thesis studies the crossing-over between causal Bayesian networks and ontologies, establishes the main correspondences between their elements and develops a cyclic approach in which we make use of the two formalisms in an interchangeable way. The rst direction involves the integration of semantic knowledge contained in the domain ontologies to anticipate the optimal choice of experimentations via a serendipitous causal discovery strategy. The semantic knowledge may contain some causal relations in addition to the strict hierarchical structure. So instead of repeating the e orts that have already been spent by the ontology developers and curators, we can reuse these causal relations by integrating them as prior knowledge when applying existing structure learning algorithms to induce partially directed causal graphs from pure observational data. To complete the full orientation of the causal network, we need to perform active interventions on the system under study. We therefore present a serendipitous decision-making strategy based on semantic distance calculus to guide the causal discovery process to investigate unexplored areas and conduct more informative experiments. The idea mainly arises from the fact that the semantically related concepts are generally the most extensively studied ones. For this purpose, we propose to supply issues for insight by favoring the experimentation on the more distant concepts according to the ontology subsumption hierarchy. The second complementary direction concerns an enrichment process by which it will be possible to reuse these causal discoveries, support the evolving character of the semantic background and make an ontology evolution. Extensive experimentations are conducted using the well-known Saccharomyces cerevisiae cell cycle microarray data and the Gene Ontology to show the merits of the SemcaDo approach in the biological eld where microarray gene expression experiments are usually very expensive to perform, complex and time consuming.
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Contributor : Montassar Ben Messaoud <>
Submitted on : Tuesday, July 10, 2012 - 10:58:43 AM
Last modification on : Wednesday, October 28, 2020 - 10:04:02 AM
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  • HAL Id : tel-00716128, version 1



Montassar Ben Messaoud. SemCaDo: une approche pour la découverte de connaissances fortuites et l'évolution ontologique. Apprentissage [cs.LG]. Université de Nantes, 2012. Français. ⟨tel-00716128⟩



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