Process model search using latent semantic analysis

Abstract

Process model similarity measures can be utilized for searching process model collections, which is also called similarity-based search. While there are quite a lot of approaches, most of them base on an underlying alignment between the activities of the compared process models. Yet, according to the results of the process model matching contests conducted in recent years, such an alignment seems to be quite difficult to achieve. The Latent Semantic Analysis-based Similarity Search approach described in this paper circumvents the matching challenge by not requiring such a matching. Instead, it uses a Latent Semantic Analysis-based Similarity Measure to query model collections and retrieve similar models. An evaluation with a collection of 80 models resulted in very good results in terms of Precision, Recall, and F-Measure. The best F-Measure value obtained during the experiments was 0.92.