A Practical Tool for Uncertainty in OWL Ontologies

S. Zhang (PRC, USA), Y. Sun, Y. Peng (USA), and X. Wang (PRC)


Semantic Web, Ontology Reasoning, Bayesian Networks


Previously we have proposed a theoretical framework, named BayesOWL, which translates an OWL taxonomy of concept classes into a Bayesian network (BN) and incorporates consistent probabilistic information about the concept classes into the translated BN. In this paper, we extend the original framework to support general OWL DL ontologies and to effectively deal with inconsistent probabilistic information. We have also implemented the BayesOWL prototype system, which can be used as a practical tool by people investigating uncertainty in Semantic Web.

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