A Probabilistic Framework for Semantic Similarity and Ontology Mapping

Authors: , Zhongli Ding, Rong Pan, Yang Yu, Boonserm Kulvatunyou, Nenad Ivezik, Albert Jones, Hyunbo Cho

Book Title: Proceedings of the 2007 Industrial Engineering Research Conference

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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 Baye- sian 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.

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