A Bayesian Approach to Uncertainty Modeling in OWL Ontology
Links to Fileshttps://ebiquity.umbc.edu/paper/html/id/204/A-Bayesian-Approach-to-Uncertainty-Modeling-in-OWL-Ontology
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Type of Work9 pages
conference papers and proceedings preprints
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Dealing 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.