A Machine Translation Like Approach to Generate Business Process Model From Textual Description

Abstract

Modeling is one of the core tasks in Business Process Management (BPM). It represents the most critical step in the BPM life cycle and is considered as a time consuming and costly task. These challenges raised the question of how researchers can save this cost by building tools that could support modeling experts in their work to reduce the manual workload. In this paper, we propose a Machine Translation (MT) like approach to deal with the problem of generating a business process model based on a textual description. We chose to follow a semantic transfer-based MT approach. Our approach consists of two main phases: The natural Language Analysis phase and BPMN diagram generation. Natural Language Analysis phase aims to analyze the text and extract the required knowledge. One of the main outputs for this phase is a Concept Map which summarizes the concepts of the related domain and the relationships between these concepts. This map represents a background for our processing in the second phase where we try to generate the "translation", which is the BPMN diagram in our case. We achieve our goal in the second phase via a set of semantic, syntactic, and morphological manipulations. The approach has been implemented and evaluated using a similarity metric based on the Graph Edit Distance. The results show that the proposed approach was able to generate models that are more than 81% similar to those created manually by a human, outperforming the state of the art in this topic.