Motivated by the necessity of reasoning about transformation experiments and their results, we propose a mapping between an ontology representing transformation processes and probabilistic relational models. Probabilistic relational models extend Bayesian networks with the notion of class and relation of relational data
bases and, for this reason, are well suited to represent concepts and ontologies’ properties. To easy the representation, we exemplify a transformation process as a cooking recipe and present our proposition for an ontology in the cooking domain that extends the Suggested Upper Merged Ontology (SUMO) upper level ontology. We present a methodology to map this ontology into a probabilistic relational model that allows reasoning in such complex domain.
In this talk I will present the approach presented at KEOD 2015 giving insights about what are the current and future works in view of the PO2 ontology that models (real) transformation processes.