Assisting Process Modeling by Identifying Business Process Elements in Natural Language Texts

Ferreira, Renato César Borges; Thom, Lucinéia Heloisa; Oliveira, José Palazzo M. de; Avila, Diego Toralles; Santos, Rubens Ideron dos; Fantinato, Marcelo

doi:10.1007/978-3-319-70625-2_15

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…Zolotarew [32] proposed a method for extracting business process from natural language text through intermediate process model using the spreadsheet-based representation. Renato [33] established a set of mapping rules associated with natural language processing techniques to identify process elements used for modeling business processes. Halioui [34] introduced an ontology-based workflow extraction framework to acquire processual knowledge from texts.…”

Section: B Process Model Extractionmentioning

confidence: 99%

Text Quality Analysis of Emergency Response Plans

et al. 2020

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Emergency response plans are regarded as effective guidance for natural disasters and these plans describe emergency response processes in natural language. More specifically, they are textual process descriptions and describe not only how all departments perform their own response tasks, but also how different departments interact with each other. Analyzing text quality of emergency response plans as a typical evaluation approach is an important concern in emergency responses. Because of the flexibility of natural language, emergency response plans normally contain unwanted ambiguities, and it is difficult to check consistency and completeness. Automatic text quality analysis of emergency response plans written in Chinese from the perspective of process descriptions is proposed in this paper. Firstly, three types of response tasks including message sending tasks, message receiving tasks and regular tasks are extracted through Bi-LSTM-CRF networks (a Conditional Random Fields network is combined with a Bidirectional Long Short-Term Memory network). Then, a series of text quality analysis rules associated with extracted response tasks are created. These rules focus on the progressive relationship of four levels of emergency responses, completeness of response tasks, ambiguity and redundancy of emergency response plans. Finally, real-world data is collected to validate the proposed approach, which consists of four types of emergency response plans of natural disasters including district, municipal, provincial and national emergency response plans. It is demonstrated that the proposed approach can be used to facilitate revisions and improvements of emergency response plans. INDEX TERMS Emergency response plans, process information extraction, text quality analysis, emergency plan evaluation, textual process descriptions.

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Section: B Process Model Extractionmentioning

confidence: 99%

Text Quality Analysis of Emergency Response Plans

et al. 2020

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“…Organizations recognize the benefit of using textual documents to capture process specifications [4,23], given that these can be created and understood by virtually everyone [11]. To allow these documents to be used for automated process analysis, such as conformance checking, a variety of techniques have been developed to extract process models from texts [11,18,22]. Other works exploit textual process specifications for model verification [2,20] or directly for process analysis [3,21].…”

Section: Related Workmentioning

confidence: 99%

“…Although their benefits are apparent, establishing declarative process models is known to be difficult, especially for domain experts that generally lack expertise in temporal logics, and in most of the cases find the graphical notation of Declare constraints unintuitive [12]. Due to these barriers to declarative model creation, a preliminary approach has been presented in [1] that automatically extracts declarative process models from natural language texts, similar to others works that have investigated the generation of imperative process models from natural language descriptions (cf., [3,11,18,22]).…”

Section: Introductionmentioning

confidence: 99%

Say It in Your Own Words: Defining Declarative Process Models Using Speech Recognition

Balder

Maggi

et al. 2020

Lecture Notes in Business Information Processing

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Declarative, constraint-based approaches have been proposed to model loosely-structured business processes, mediating between support and flexibility. A notable example is the Declare framework, equipped with a graphical declarative language whose semantics can be characterized with several logic-based formalisms. Up to now, the main problem hampering the use of Declare constraints in practice has been the difficulty of modeling them: Declare's formal notation is difficult to understand for users without a background in temporal logic, whereas its graphical notation has been shown to be unintuitive. Therefore, in this work, we present and evaluate an analysis toolkit that aims at bypassing this issue by providing users with the possibility to model Declare constraints using their own way of expressing them. The toolkit contains a Declare modeler equipped with a speech recognition mechanism. It takes as input a vocal statement from the user and converts it into the closest (set of) Declare constraint(s). The constraints that can be modeled with the tool cover the entire Multi-Perspective extension of Declare (MP-Declare), which complements control-flow constraints with data and temporal perspectives. Although we focus on Declare, the work presented in this paper represents the first attempt to test the feasibility of speech recognition in business process modeling as a whole.

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