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Ontologies and probabilistic graphical models are considered within the most efficient frameworks in knowledge representation. Ontologies are the key concept in semantic technology whose use is increasingly prevalent by the computer science community. They provide a structured representation of knowledge characterized by its semantic richness. Probabilistic Graphical Models (PGMs) are powerful tools for representing and reasoning under uncertainty. Nevertheless, both suffer from their building phase. It is well known that learning the structure of a PGM and automatic ontology enrichment are very hard problems. Therefore, several algorithms have been proposed for learning the PGMs structure from data and several others have led to automate the process of ontologies enrichment. However, there was not a real collaboration between these two research directions. In this work, we propose a two-way approach that allows PGMs and ontologies cooperation. More precisely, we propose to harness ontologies representation capabilities in order to enrich the building process of PGMs. We are in particular interested in object oriented Bayesian networks (OOBNs) which are an extension of standard Bayesian networks (BNs) using the object paradigm. We first generate a prior OOBN by morphing an ontology related to the problem under study and then, we describe how the learning process carried out with the OOBN might be a potential solution to enrich the ontology used initially.

A TWO-WAY APPROACH FOR PROBABILISTIC GRAPHICAL MODELS STRUCTURE LEARNING AND ONTOLOGY ENRICHMENT