Semantically-Linked Bayesian Networks: A Framework for Probabilistic Inference Over Multiple Bayesian Networks
by Rong Pan
Wednesday, August 2, 2006, 13:00pm - Wednesday, August 2, 2006, 15:00pm
325b ITE, UMBC
In this thesis, we propose a theoretical framework, named Semantically-Linked Bayesian Networks (SLBN), to fill this blank. SLBN is distinguished from existing work in that it defines linkages between semantically similar variables and probabilistic influences are carried by variable linkage from one BN to another by soft evidences and virtual evidences. To support SLBN’s inference, we have developed two algorithms for belief update with soft evidences. Both of these algorithms have clear computational and practical advantages over the methods proposed by others in the past. To justify SLBN’s inference process, we propose J-graph to represent the jointed knowledge of the linked BNs and the variable linkages. Finally, SLBN is applied to the problem of concept mapping between semantic web ontologies.
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