A Probabilistic Framework for Semantic Similarity and Ontology Mapping



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 Bayes- ian 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 two components. This framework ex- pands the semantic web and can serve as a theoretical basis for solving real world semantic integr ation problems.

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Available from: Albert Jones, Sep 25, 2014
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