Process model matching is a key activity in many business process management tasks. It is an activity that consists of detecting an alignment between process models by finding similar activities in two process models. This article proposes a method based on WordNet glosses to improve the effectiveness of process model matchers. The proposed method is composed of three steps. In the first step, all activities of the two BPs are extracted. Second, activity labels are expanded using word glosses and finally, similar activities are detected using the cosine similarity metric. Two experiments were conducted on well-known datasets to validate the effectiveness of the proposed approach. In the first one, an alignment is computed using the cosine similarity metric only and without a process of expansion. While, in the second experiment, the cosine similarity metric is applied to the expanded activities using glosses. The results of the experiments were promising and show that expanding activities using WordNet glosses improves the effectiveness of process model matchers.
Cross-view kernel similarity metric learning using pairwise constraints for person re-identification
