Chapter

The Semantic Web – ISWC 2005

Volume 3729 of the series Lecture Notes in Computer Science pp 563-577

A Bayesian Network Approach to Ontology Mapping

  • Rong PanAffiliated withCarnegie Mellon UniversityDepartment of Computer Science and Electrical Engineering, University of Maryland, Baltimore County
  • , Zhongli DingAffiliated withCarnegie Mellon UniversityDepartment of Computer Science and Electrical Engineering, University of Maryland, Baltimore County
  • , Yang YuAffiliated withCarnegie Mellon UniversityDepartment of Computer Science and Electrical Engineering, University of Maryland, Baltimore County
  • , Yun PengAffiliated withCarnegie Mellon UniversityDepartment of Computer Science and Electrical Engineering, University of Maryland, Baltimore County

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Abstract

This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling uncertainty in semantic web. In this approach, the source and target ontologies are first translated into Bayesian networks (BN); the concept mapping between the two ontologies are treated as evidential reasoning between the two translated BNs. Probabilities needed for constructing conditional probability tables (CPT) during translation and for measuring semantic similarity during mapping are learned using text classification techniques where each concept in an ontology is associated with a set of semantically relevant text documents, which are obtained by ontology guided web mining. The basic ideas of this approach are validated by positive results from computer experiments on two small real-world ontologies.