A Bayesian Approach to Uncertainty Modeling in OWL Ontology

Authors: Zhongli Ding, Yun Peng, and Rong Pan

Book Title: Proceedings of the International Conference on Advances in Intelligent Systems - Theory and Applications

Date: November 15, 2004

Abstract: 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.

Type: InProceedings

Address: Luxembourg

Note: Has three GS keys: UMdqaFCbMakJ, M2aqATowR4cJ, GTtycwLF6z0J

Pages: 9

Tags: bayesian reasoning, ipfp, ontology, semantic web, uncertainty

Google Scholar: GTtycwLF6z0J

Number of Google Scholar citations: 2 [show citations]

Number of downloads: 1681

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 Bayes OWL.

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