Scalable uncertainty treatment using triplestores and the OWL 2 RL profile

The probabilistic ontology language PR-OWL (Probabilistic OWL) uses Multi-Entity Bayesian Networks (MEBN), an extension of Bayesian networks with first-order logic, to add the ability to deal with uncertainty to OWL, the main language of the Semantic Web. A second version, PR-OWL 2, was proposed to allow the construction of hybrid ontologies, containing deterministic and probabilistic parts. Existing PROWL implementations cannot deal with very large assertive databases. This limitation is a main obstacle for applying the language in real domains, such as Maritime Domain Awareness (MDA). This paper proposes a PR-OWL extension using RDF triplestores and the OWL 2 RL profile, based on rules, in order to allow dealing with uncertainty in ontologies with millions of assertions. We illustrate our ideas with an MDA ontology built for the PROGNOS (PRobabilistic OntoloGies for Net-centric Operation Systems) project.