Abstract
Decision models are strategic for formalizing how data influences the main decisions in a organization. Due to its importance, standard notations like DMN have appeared in recent years, to serve as a central resource for synchronizing the people and systems with respect to decisions. However, the modeling of DMN specifications can be tedious and error-prone, hampering its adoption in practice. This paper presents a technique to automatically obtain complete DMN models from textual descriptions. The technique, grounded in natural language processing combined with tailored syntactic patterns, allows to extract both the decision requirements and the decision logic described in a text. Our experimental evaluation shows promising results, even for the quite small pattern set used.
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Acknowledgments
This work has been supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R, and by the Ecuadorian National Secretary of Higher Education, Science and Technology (SENESCYT).
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Quishpi, L., Carmona, J., Padró, L. (2021). Extracting Decision Models from Textual Descriptions of Processes. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds) Business Process Management. BPM 2021. Lecture Notes in Computer Science(), vol 12875. Springer, Cham. https://doi.org/10.1007/978-3-030-85469-0_8
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