Extracting Declarative Process Models from Natural Language
- 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 closelyProcess 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.…
Author details: | Aa Han van derORCiDGND, Claudio Di CiccioORCiDGND, Henrik LeopoldORCiDGND, Hajo A. ReijersORCiDGND |
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DOI: | https://doi.org/10.1007/978-3-030-21290-2_23 |
ISBN: | 978-3-030-21290-2 |
ISBN: | 978-3-030-21289-6 |
ISSN: | 0302-9743 |
ISSN: | 1611-3349 |
Title of parent work (English): | Advanced Information Systems Engineering (CAISE 2019) |
Publisher: | Springer |
Place of publishing: | Cham |
Publication type: | Other |
Language: | English |
Date of first publication: | 2019/05/29 |
Publication year: | 2019 |
Release date: | 2021/05/03 |
Tag: | Declarative modelling; Model extraction; Natural language processing |
Volume: | 11483 |
Number of pages: | 18 |
First page: | 365 |
Last Page: | 382 |
Funding institution: | EU H2020 programme under MSCA-RISE [645751]; Alexander von Humboldt FoundationAlexander von Humboldt Foundation |
Organizational units: | Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke |
Peer review: | Referiert |