Learning Probabilistic Relational Models using co-clustering methods

Abstract : Probabilistic Relational Models (PRM) are probabilistic graphical models which define a factored joint distribution over a set of random variables in the context of relational datasets. While regular PRM define probabilistic dependencies between objects' descriptive attributes, an extension called PRM with Reference Uncertainty (PRM-RU) allows in addition to manage link uncertainty between them, by adding random variables called selectors. In order to avoid problems due to large variables domains, selectors are associated with partition functions, mapping objects to a set of clusters, and selectors' distributions are defined over the set of clusters. In PRM-RU, the definition of partition functions constrain us to learn them using flat (i.e. non relational) clustering algorithms. However, many relational clustering techniques show better results in this context. Among them, co-clustering algorithms, applied on binary relationships, focus on simultaneously clustering both entities objects to use as much information available from the relationship as possible. In this paper, we present a work in progress about a new extension of PRM, called PRM with Co-Reference Uncertainty, which associates, to each class containing reference slots, a single selector and a single co-partition function learned using a co-clustering algorithm.
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Anthony Coutant, Philippe Leray, Hoel Le Capitaine. Learning Probabilistic Relational Models using co-clustering methods. Structured Learning: Inferring Graphs from Structured and Unstructured Inputs (SLG 2013) ICML Workshop, 2013, Atlanta, United States. ⟨hal-00819031⟩

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