Reusing Models of Different Abstraction Levels

Reuse of models assists in constructing a new model on the basis of existing knowledge, by retrieving a model that matches a preliminary partial input model. It often employs similarity measures for identifying reusable models that are structurally and semantically similar to the input model. However, in many cases the preliminary input model is of a higher level of abstraction than the detailed models to be retrieved. Hence, structural similarity cannot be detected. This paper proposes the concept of structural equivalence, which means that a detailed model is a refinement of an abstract input model. Measuring structural equivalence rather than structural similarity enables retrieving an appropriate model despite differences in the abstraction level between the input model and the models to be reused.