, Volume 11, Issue 5, pp 1132-1136

RETRACTED ARTICLE: Uncertainty modeling based on Bayesian network in ontology mapping

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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.

Foundation item: Supported by the National Natural Science Foundation of China (60403027)
Biography: LI Yuhua(1968-), female, Associate professor, research direction: semantic Web, data mining.
The manuscript “Uncertainty Modeling Based on Bayesian Network in Ontology Mapping” written by Li Yuhua et al. published in Wuhan University Journal of Natural Sciences, Vol. 11, No. 5 (2006) has to be retracted as a thorough investigation has revealed that it contains many contents same to the following three articles (or theses)
[1] Zhongli Ding et al. A Bayesian Approach to Uncertainty Modeling in OWL Ontology in Proceedings of the International Conference on Advances in Intelligent Systems― Theory and Applications, November 2004, Luxembourg.
[2] Yun Peng, Zhongli Ding et al. Modifying Bayesian Networks by Probability Constraints in Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence (UAI-2005), Edinburgh, Scotland, July 26-29, 2005.
[3] Zhongli Ding. BayesOWL: A Probabilistic Framework for Uncertainty in Semantic Web Ph.D. Thesis of University of Maryland, 2005.
The article should therefore be considered as non-existent and not be cited or referred to in the future. Li Yuhua et al apologize to the scientific community.