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 modules. This framework expands the semantic web and can serve as a theoretical basis for solving real world semantic integration problems.
Proceedings Title: Proceedings of the 2007 Industrial Engineering Research Conference
Conference Dates: May 7-12, 2007
Conference Location: Orlando, FL
Pub Type: Conferences
Bayesian networks, integration, ontology, Semantic web, uncertainty