To address the difficult but important problem of modeling uncertainty in semantic web, this research has taken a probabilistic approach and developed a theoretical framework, named BayesOWL, that incorporates the Bayesian network (BN), a widely used graphic model for probabilistic interdependency, into the web ontology language OWL. This framework consists of three key components:

• a representation for encoding the probability distributions as OWL classes;
• a set of structural translation rules and procedures that converts an OWL taxonomy ontology into a BN directed acyclic graph (DAG); and
• a method SD-IPFP based on "iterative proportional fitting procedure" (IPFP) that incorporates available probability constraints into the conditional probability tables (CPTs) of the translated BN.

The translated BN, which preserves the semantics of the original ontology and is consistent with all the given probability constraints, can support ontology reasoning, both within and cross ontologies, as Bayesian inferences with more accurate and more plausible results.

Method SD-IPFP has been further developed into D-IPFP, a general approach for modifying BN with probability constraints that goes beyond BayesOWL. To empirically validate this theoretical work, both BayesOWL and variations of IPFP have been implemented and tested with example ontologies and probability constraints. The test confirms theoretical analysis.