Ranking-based Evaluation of Process Model Matching

Process model matching refers to the automatic detection of semantically equivalent or similar activities between two process models. The output of process model matchers is the basis for many advanced process model analysis techniques and, therefore, must be as accurate as possible. Measuring the performance of process model matchers, however, is a difficult task. On the one hand, it is hard to define which correspondences are actually correct. On the other hand, it is challenging to appropriately take the output of matchers into account, because they often produce confidence values between zero and one. In this paper, we propose the first evaluation procedure for process model matchers that addresses both of these challenges. The core idea is to rank both the computed and the desired correspondences based on their confidence values and compare them using the Spearman’s rank correlation coefficient. We perform an in-depth evaluation in which we apply the new evaluation procedure and illustrate how it helps gaining interesting insights.