Uncertainty Modeling Based on Bayesian Network in Ontology Mapping

0 IntroductionIn many semantic interoperability applications , ontologymappingis the first step to be resolved1].If we want toget exact mappinginformation,we needto deal withthe prob-lemof uncertainty2].Uncertainty becomes more prevalent in concept mappingbetween two ontologies . Semantic si milarities between con-cepts are difficult to represent logically,but can easily be re-presented probabilistically.This has motivatedrecent develop-ment of ontology mapping taking probabilistic approach…

Uncertainty Modeling Based on Bayesian Network in Ontology Mapping

How to deal with uncertainty is crucial in exact concept mapping between ontologies. This paper presents a new framework on modeling uncertainty in ontologies based on bayesian networks (BN). In our approach, ontology Web language (OWL) is extended to add probabilistic markups for attaching probability information, the source and target ontologies (expressed by patulous OWL) are translated into bayesian networks (BNs), the mapping between the two ontologies can be digged out by constructing the conditional probability tables (CPTs) of the BN using a improved algorithm named I-IPFP based on iterative proportional fitting procedure (IPFP). The basic idea of this framework and algorithm are validated by positive results from computer experiments.