Bayesian Reasoning Over Models

A crucial part of verifying and validating models is the iden- tification of inconsistencies. Inconsistencies can exist whenever models overlap semantically. Such overlaps are predominant in model-driven en- gineering, where the use of multiple viewpoints leads to a variety of in- complete representations of one or more aspects of a system. While the commonly employed rule-based approaches to identifying inconsistencies can be eective, state of the art methods for inferring or determining semantic overlaps are not. Techniques relying on unification algorithms or a unifying ontology make strong assumptions, are error prone and can be costly to maintain. In this paper, an alternative approach based on Bayesian reasoning is proposed. We show how Bayesian inference com- bined with pattern matching can be used to infer likely semantic overlaps in models. The approach is illustrated and evaluated using the inference of semantic equivalences as an example of inferring one type of semantic overlap.