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  • Semantically-Linked Bayesian Networks (SLBN)

At the present time, Bayesian networks (BNs), presumably the most popular uncertainty inference framework, are still widely used as standalone systems. When the problem itself is distributed, domain knowledge has to be centralized and unified before a single BN can be created. Alternatively, separate BNs describing related sub-domains or different aspects of the same domain may be created, but it is difficult to combine them for problem solving even if the interdependent relations between variables across these BNs are available. Existing approaches have greatly restricted expressiveness and applicability as they either impose very strong constraints on the distributed domain knowledge or only focus on a specific application. What is missing is a principled framework that can support probabilistic inference over separately developed BNs.

Semantically-Linked Bayesian Networks (SLBN) is proposed 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. SLBN has been applied to the problem of concept mapping between semantic web ontologies.