research-article

Natural Language Processing in Business Process Identification and Modeling: A Systematic Literature Review

Authors:

Ana Cláudia de Almeida Bordignon,

Lucinéia Heloisa Thom,

Thanner Soares Silva,

Vinicius Stein Dani,

Marcelo Fantinato,

Renato Cesar Borges FerreiraAuthors Info & Claims

SBSI '18: Proceedings of the XIV Brazilian Symposium on Information Systems

Article No.: 25, Pages 1 - 8

https://doi.org/10.1145/3229345.3229373

Published: 04 June 2018 Publication History

Abstract

Business Process Management (BPM) has been receiving increasing attention in recent years. Many organizations have been adapting their business to a process-centered view since they started noticing its potential to reduce costs, improve productivity and achieve higher levels of quality. However, implementing BPM in organizations requires time, making the automation of process identification and discovery highly desirable. To achieve this expectation, the application of Natural Language Processing (NLP) techniques and tools has emerged to generate process models from unstructured text. In this paper, we provide the results of a systematic literature review conducted in preparation and processing of natural language text aiming the extraction of business processes and process quality assurance. The study presents techniques applied to the BPM life-cycle phases of process identification, process discovery and process analysis as well as tools to support process discovery. This review covered papers from 2009 up to 2016 and identifies 518 articles of which 33 were selected as relevant to our work. The results of the present study may be valuable to support research in extraction of business process models from natural language text.

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Cited By

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https://doi.org/10.1080/12460125.2024.2410040
Mößlang MBernsteiner RPloder CSchlögl S(2024)Automatic Generation of a Business Process Model Diagram Based on Natural Language ProcessingKnowledge Management in Organisations10.1007/978-3-031-63269-3_18(237-247)Online publication date: 22-Jun-2024
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Ayad SAlSayoud F(2024)Exploring ChatGPT Prompt Engineering for Business Process Models Semantic Quality ImprovementGood Practices and New Perspectives in Information Systems and Technologies10.1007/978-3-031-60221-4_39(412-422)Online publication date: 13-May-2024
https://doi.org/10.1007/978-3-031-60221-4_39
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Natural Language Processing in Business Process Identification and Modeling: A Systematic Literature Review

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SBSI '18: Proceedings of the XIV Brazilian Symposium on Information Systems

June 2018

578 pages

ISBN:9781450365598

DOI:10.1145/3229345

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMIS: ACM Special Interest Group on Management Information Systems
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Published: 04 June 2018

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SBSI'18: XIV Brazilian Symposium on Information Systems

June 4 - 8, 2018

Caxias do Sul, Brazil

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Cited By

Arslan MMunawar SCruz C(2024)Business insights using RAG–LLMs: a review and case studyJournal of Decision Systems10.1080/12460125.2024.2410040(1-30)Online publication date: 3-Oct-2024
https://doi.org/10.1080/12460125.2024.2410040
Mößlang MBernsteiner RPloder CSchlögl S(2024)Automatic Generation of a Business Process Model Diagram Based on Natural Language ProcessingKnowledge Management in Organisations10.1007/978-3-031-63269-3_18(237-247)Online publication date: 22-Jun-2024
https://doi.org/10.1007/978-3-031-63269-3_18
Ayad SAlSayoud F(2024)Exploring ChatGPT Prompt Engineering for Business Process Models Semantic Quality ImprovementGood Practices and New Perspectives in Information Systems and Technologies10.1007/978-3-031-60221-4_39(412-422)Online publication date: 13-May-2024
https://doi.org/10.1007/978-3-031-60221-4_39
Casciani ABernardi MCimitile MMarrella A(2024)Conversational Systems for AI-Augmented Business Process ManagementResearch Challenges in Information Science10.1007/978-3-031-59465-6_12(183-200)Online publication date: 2-May-2024
https://doi.org/10.1007/978-3-031-59465-6_12
Schüler SAlpers S(2024)State of the Art: Automatic Generation of Business Process ModelsBusiness Process Management Workshops10.1007/978-3-031-50974-2_13(161-173)Online publication date: 11-Jan-2024
https://doi.org/10.1007/978-3-031-50974-2_13
Baïna KEl Hamlaoui MKabbaj H(2020)Business Process Modelling AugmentedProceedings of the 13th International Conference on Intelligent Systems: Theories and Applications10.1145/3419604.3419793(1-6)Online publication date: 23-Sep-2020
https://dl.acm.org/doi/10.1145/3419604.3419793

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