Bayesian Approach to Uncertainty Modelling in OWL Ontology

摘要

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.