A Probabilistic Framework for Semantic Similarity and Ontology Mapping

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https://ebiquity.umbc.edu/paper/html/id/389/A-Probabilistic-Framework-for-Semantic-Similarity-and-Ontology-Mapping

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http://hdl.handle.net/11603/12129

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UMBC Computer Science and Electrical Engineering Department
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Date

2007-05-19

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conference papers and proceedings preprints
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law
Public Domain Mark 1.0

Subjects

Semantic web
uncertainty
integration
ontology
Bayesian networks
UMBC Ebiquity Research Group

Abstract

We propose a probabilistic framework to address uncertainty in ontology-based semantic integration and interopera- tion. This framework consists of three main components: 1) BayesOWL that translates an OWL ontology to a Bayesian network, 2) SLBN (Semantically Linked Bayesian Networks) that support reasoning across translated BNs, and 3) a Learner that learns from the web the probabilities needed by the other modules. This framework expands the semantic web and can serve as a theoretical basis for solving real world semantic integration problems.