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.