A new flexible method for advising metamodel matching

One relevant issue in metamodel matching is how to select the most suitable matching technique to execute for a given couple of metamodels, and how to adjust parameters (e.g., threshold, F-measure, quality). In this paper, we present a flexible method for selecting the most appropriate metamodel matching technique for a given couple of metamodels. The proposed method assists the user to choose the most suitable matching technique that provides good quality of matches. This method relies on a new quality metric called Score and, on using a decision tree. In order to validate our method, we conduct experimental results on ten real-world metamodels and four recent matching techniques.