A Methodological Approach for Classifying and Differentiating Business Processes Using Tasks Label

Abstract

It is crucial for two organizations to integrate their business process models when one acquires the other or merges with another organization. This research is motivated by the problem of matching processes with semantically similar activity names and similar process maps that make alignment difficult. Introducing a new approach based on the Domain Specification Mapper along with the current state-of-art transformer models, BERT for semantic augmentation and contextual interpretation of activity labels. The method combines semantic analysis, syntactic features and machine learning algorithms to show that DSM does not only increase the effectiveness of the transformer models such as BERT, but also increases the effectiveness of other machine learning models. Although traditional classifiers and semantic syntactic features failed to provide a correct classification of processes, DSM with BERT can successfully address these problems. Furthermore, the integration of DSM with other classifiers also showed good results and improved the classification rate. This approach facilitates integration of process, increases accuracy and aligns operations which makes it ideal for mergers and acquisitions.