Abstract
This paper addresses a major weakness of current technologies for the Semantic Web, namely the lack of a principled means to represent and reason about uncertainty. This not only hinders the realization of the original vision for the Semantic Web, but also creates a barrier to the development of new, powerful features for general knowledge applications that require proper treatment of uncertain phenomena. We present PR-OWL, a probabilistic extension to the OWL web ontology language that allows legacy ontologies to interoperate with newly developed probabilistic ontologies. PR-OWL moves beyond the current limitations of deterministic classical logic to a full first-order probabilistic logic. By providing a principled means of modeling uncertainty in ontologies, PR-OWL can be seen as a supporting tool for many applications that can benefit from probabilistic inference within an ontology language, thus representing an important step toward the W3C’s vision for the Semantic Web. In order to fully present the concepts behind PR-OWL, we also cover Multi-Entity Bayesian Networks (MEBN), the Bayesian first-order logic supporting the language, and UnBBayes-MEBN, an open source GUI and reasoner that implements PR-OWL concepts. Finally, a use case of PR-OWL probabilistic ontologies is illustrated here in order to provide a grasp of the potential of the framework.
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da Costa, P.C.G., Laskey, K.B., Laskey, K.J. (2008). PR-OWL: A Bayesian Ontology Language for the Semantic Web. In: da Costa, P.C.G., et al. Uncertainty Reasoning for the Semantic Web I. URSW URSW URSW 2006 2007 2005. Lecture Notes in Computer Science(), vol 5327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89765-1_6
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