A Bayesian Approach to Uncertainty Modelling in OWL Ontology
Zhongli Ding, Yun Peng, Rong Pan · Defense Technical Information Center (DTIC) · 2006
Dealing with uncertainty is crucial in ontology engineering tasks such as domain modelling, ontology reasoning, and concept mapping between ontologies. This paper presents the authors' on-going research on modelling uncertainty in ontologies based on Bayesian networks (BN). The work includes the following: (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 proportional fitting procedure (IPFP). The translated BN can support more accurate ontology reasoning under uncertainty as Bayesian inferences.