Abstract
Process modeling is a complex and important task in any business process management project. Gathering information to build a process model needs effort by analysts in different ways, such as interviews and document review. However, this documentation is not always well structured and can be difficult to be understood. Thus, techniques that allow the structuring and recognition of process elements in the documentation can help in the understanding of the process and, consequently, in the modeling activity. In this context, this paper proposes an approach to recognize business process elements in natural language texts. We defined a set of 32 mapping rules to recognize business process elements in texts using natural language processing techniques and which were identified through an empirical study in texts containing descriptions of a process. Furthermore, a prototype was developed and it showed promising results. The analyses of 70 texts revealed 73.61% precision, 70.15% recall and 71.82% F-measure. Moreover, two surveys showed that 93.33% of participants agree with the mapping rules and that the approach helps the analysts in both the time spent and the effort made in the process modeling task. This paper is a reiteration and an evolution of the work presented in Ferreira et al. [1].
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Notes
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Ferreira, R.C.B., Silva, T.S., Avila, D.T., Thom, L.H., Fantinato, M. (2018). Recognition of Business Process Elements in Natural Language Texts. In: Hammoudi, S., Śmiałek, M., Camp, O., Filipe, J. (eds) Enterprise Information Systems. ICEIS 2017. Lecture Notes in Business Information Processing, vol 321. Springer, Cham. https://doi.org/10.1007/978-3-319-93375-7_27
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