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Interactive Causal Discovery in Knowledge Graphs

Abstract : Being able to provide explanations about a domain is a hard task that requires from a probabilistic reasoning's viewpoint a causal knowledge about the domain variables, allowing one to predict how they can influence each others. However, causal discovery from data alone remains a challenging question. In this article, we introduce a way to tackle this question by presenting an interactive method to build a probabilistic relational model from any given relevant domain represented by a knowledge graph. Combining both ontological and expert knowledge, we define a set of constraints translated into a so-called relational schema. Such a relational schema can then be used to learn a probabilistic relational model, which allows causal discovery.
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Contributor : Mélanie Munch <>
Submitted on : Monday, November 18, 2019 - 2:18:53 PM
Last modification on : Tuesday, August 4, 2020 - 11:24:02 AM


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  • HAL Id : hal-02368242, version 1


Mélanie Munch, Juliette Dibie-Barthelemy, Pierre-Henri Wuillemin, Cristina Manfredotti. Interactive Causal Discovery in Knowledge Graphs. PROFILES/SEMEX@ISWC 2019, Oct 2019, Auckland, New Zealand. pp.78-93. ⟨hal-02368242⟩



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