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2019 | OriginalPaper | Chapter

Extracting Declarative Process Models from Natural Language

Authors : Han van der Aa, Claudio Di Ciccio, Henrik Leopold, Hajo A. Reijers

Published in: Advanced Information Systems Engineering

Publisher: Springer International Publishing

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Abstract

Process models are an important means to capture information on organizational operations and often represent the starting point for process analysis and improvement. Since the manual elicitation and creation of process models is a time-intensive endeavor, a variety of techniques have been developed that automatically derive process models from textual process descriptions. However, these techniques, so far, only focus on the extraction of traditional, imperative process models. The extraction of declarative process models, which allow to effectively capture complex process behavior in a compact fashion, has not been addressed. In this paper we close this gap by presenting the first automated approach for the extraction of declarative process models from natural language. To achieve this, we developed tailored Natural Language Processing techniques that identify activities and their inter-relations from textual constraint descriptions. A quantitative evaluation shows that our approach is able to generate constraints that closely resemble those established by humans. Therefore, our approach provides automated support for an otherwise tedious and complex manual endeavor.

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previous chapter DIA: Stream Analytics on User-Defined Event Intervals

next chapter From Process Models to Chatbots

See: www.clres.com/db/classes/ClassTemporal.php.

See: https://github.com/hanvanderaa/declareextraction.

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Title: Extracting Declarative Process Models from Natural Language
Authors: Han van der Aa
Claudio Di Ciccio
Henrik Leopold
Hajo A. Reijers
Publisher: Springer International Publishing
Book: Advanced Information Systems Engineering
Print ISBN: 978-3-030-21289-6

Electronic ISBN: 978-3-030-21290-2

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-21290-2_23

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