BayesOWL: A Probabilistic Framework for Uncertainty in Semantic Web
To address the difficult but important problem of modeling uncertainty in semantic web, this research has taken a probabilistic approach and developed a theoretical framework, named BayesOWL, that incorporates the Bayesian network (BN), a widely used graphic model for probabilistic interdependency, into the web ontology language OWL. This framework consists of three key components:
The translated BN, which preserves the semantics of the original ontology and is consistent with all the given probability constraints, can support ontology reasoning, both within and cross ontologies, as Bayesian inferences with more accurate and more plausible results.
Method SD-IPFP has been further developed into D-IPFP, a general approach for modifying BN with probability constraints that goes beyond BayesOWL. To empirically validate this theoretical work, both BayesOWL and variations of IPFP have been implemented and tested with example ontologies and probability constraints. The test confirms theoretical analysis.
Authors: Zhongli Ding
Date: December 05, 2005
Format: Microsoft PowerPoint (Need a reader? Get one here)
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