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.

Extracting declarative process models from natural language / van der Aa, H.; Di Ciccio, C.; Leopold, H.; Reijers, H. A.. - 11483:(2019), pp. 365-382. (Intervento presentato al convegno 31st International Conference on Advanced Information Systems Engineering, CAiSE 2019 tenutosi a Rome; Italy) [10.1007/978-3-030-21290-2_23].

Extracting declarative process models from natural language

Di Ciccio C.;
2019

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.
2019
31st International Conference on Advanced Information Systems Engineering, CAiSE 2019
declarative modelling; model extraction; natural language processing
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Extracting declarative process models from natural language / van der Aa, H.; Di Ciccio, C.; Leopold, H.; Reijers, H. A.. - 11483:(2019), pp. 365-382. (Intervento presentato al convegno 31st International Conference on Advanced Information Systems Engineering, CAiSE 2019 tenutosi a Rome; Italy) [10.1007/978-3-030-21290-2_23].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1362083
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