Ding, ZhongliPeng, YunPan, Rong2018-12-122018-12-122004-11-15http://hdl.handle.net/11603/12236Proceedings of the International Conference on Advances in Intelligent Systems - Theory and ApplicationsDealing with uncertainty is crucial in ontology engineering tasks such as domain modeling, ontology reasoning, and concept mapping between ontologies. This paper presents our on-going research on modeling uncertainty in ontologies based on Bayesian networks (BN). This includes 1) extending OWL to allow additional probabilistic markups for attaching probability information, 2) directly converting a probabilistically annotated OWL ontology into a BN structure by a set of structural translation rules, and 3) constructing the conditional probability tables (CPTs) of this BN using a new method based on iterative proportiobal fitting procedure (IPFP). The translated BN can support more accurate ontology reasoning under uncertainty as Bayesian inferences.9 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.bayesian networksontologysemantic webuncertainityUMBC Ebiquity Research GroupA Bayesian Approach to Uncertainty Modeling in OWL OntologyText