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Improving uncertain reasoning combining probabilistic relational models and expert knowledge

Abstract : This thesis focuses on integrating expert knowledge to enhance reasoning under uncertainty. Our goal is to guide the probabilistic relations’ learning with expert knowledge for domains described by ontologies.To do so we propose to couple knowledge bases (KBs) and an oriented-object extension of Bayesian networks, the probabilistic relational models (PRMs). Our aim is to complement the statistical learning with expert knowledge in order to learn a model as close as possible to the reality and analyze it quantitatively (with probabilistic relations) and qualitatively (with causal discovery). We developped three algorithms throught three distinct approaches, whose main differences lie in their automatisation and the integration (or not) of human expert supervision.The originality of our work is the combination of two broadly opposed philosophies: while the Bayesian approach favors the statistical analysis of the given data in order to reason with it, the ontological approach is based on the modelization of expert knowledge to represent a domain. Combining the strenght of the two allows to improve both the reasoning under uncertainty and the expert knowledge.
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Submitted on : Thursday, March 25, 2021 - 2:20:20 PM
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Mélanie Munch. Improving uncertain reasoning combining probabilistic relational models and expert knowledge. General Mathematics [math.GM]. Université Paris-Saclay, 2020. English. ⟨NNT : 2020UPASB011⟩. ⟨tel-03181149⟩



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