Process Model Generation from Textual Descriptions

Abstract

Process models are an important source to gather the information on organizational workflows and also represent the first point of process analysis and improvement. In organizations, business process modeling is an important tool to understand and automate the business processes. However, in many organizations the existing documentation of business processes is difficult to understand by the analyst. So, in real-life organizations, the high complexity of business processes is constantly raising an issue. Due to this, the establishment of process models for the business process is becoming a challenge for the stakeholders. So, the process model extraction from the business processes may helpful to minimize the process modeling effort. But, manual generation of business process models is a time taking task for the stakeholders. Yet, to assist this task, new methods can be implemented for the automation of process design phase. An approach to generate process models from the textual descriptions has been developed. In this approach, we use different natural language processing techniques to define a set of mapping rules. Through these mapping rules, we extract the elements business process models from textual descriptions and also checked the correct sequence of extracted elements for business process models. By combining the business process elements according to their sequence, process model is generated