Article

Data-Driven Annotation of Textual Process Descriptions Based on Formal Meaning Representations

Authors:

Lars Ackermann,

Julian Neuberger,

Stefan JablonskiAuthors Info & Claims

Advanced Information Systems Engineering: 33rd International Conference, CAiSE 2021, Melbourne, VIC, Australia, June 28 – July 2, 2021, Proceedings

Pages 75 - 90

https://doi.org/10.1007/978-3-030-79382-1_5

Published: 28 June 2021 Publication History

Abstract

Business process management encompasses a variety of tasks that can be solved system-aided but usually require formal process representations, i.e. process models. However, it requires a significant effort to learn a formal process modeling language like, for instance, BPMN. Among others, this is one reason why companies often still stick to informal textual process descriptions. However, in contrast to formal models, information from natural language text usually cannot be automatically processed by algorithms. Hence, recent research also focuses on annotated textual process descriptions to make text machine processable.

While still human-readable, they additionally contain annotations following a formal scheme. Thus, they also enable automated processing by, for instance, formal reasoning and simulation. State-of-the-art techniques for automatically annotating textual process descriptions are either based on hand-crafted rule sets or artificial neural networks. Maintaining complex rule sets requires a significant manual effort and the approaches using neural networks suffer from rather low result quality. In this paper we present an approach based on Semantic Parsing and Graph Convolutional Networks that avoids manually defined rules and provides significantly better results than existing techniques based on neural networks. A comprehensive evaluation using multiple data sets from both academia and industry shows encouraging results and differentiates between several applied text features.

References

[1]

van der Aa, H., Carmona, J., Leopold, H., Mendling, J., Padró, L.: Challenges and opportunities of applying natural language processing in business process management. In: Proceedings of COLING. ACL (2018)

[2]

van der Aa H, Di Ciccio C, Leopold H, and Reijers HA Giorgini P and Weber B Extracting declarative process models from natural language Advanced Information Systems Engineering 2019 Cham Springer 365-382

[3]

van der Aa H, Leopold H, and Reijers HA Motahari-Nezhad HR, Recker J, and Weidlich M Detecting inconsistencies between process models and textual descriptions Business Process Management 2015 Cham Springer 90-105

[4]

Aalst W Data science in action Process Mining 2016 Heidelberg Springer 3-23

[5]

Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: Proceedings of OSDI (2016)

[6]

Abend, O., Rappoport, A.: Universal conceptual cognitive annotation (UCCA). In: Proceedings of the ACL. ACL (2013)

[7]

Abend, O., Rappoport, A.: The state of the art in semantic representation. In: Proceedings of the ACL. ACL (2017)

[8]

Allen-Zhu, Z., Li, Y., Liang, Y.: Learning and generalization in overparameterized neural networks, going beyond two layers. In: Proceedings of NeurIPS (2019)

[9]

Btoush, E.S., Hammad, M.M.: Generating ER diagrams from requirement specifications based on natural language processing. In: IJDTA (2015)

[10]

Che, W., Dou, L., Xu, Y., Wang, Y., Liu, Y., Liu, T.: HIT-SCIR at MRP 2019: a unified pipeline for meaning representation parsing via efficient training and effective encoding. In: Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 CoNLL (2019)

[11]

Chinchor, N., Sundheim, B.: Muc-5 evaluation metrics. In: Proceedings of MUC. ACL (1993)

[12]

Dawood, O.S., et al.: From requirements engineering to UML using natural language processing-survey study. In: EJERS (2017)

[13]

Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. Language, Speech, and Communication. MIT Press (1998)

[14]

Figl K and Recker J Exploring cognitive style and task-specific preferences for process representations Requirements Eng. 2014 21 1 63-85

[15]

Friedrich F, Mendling J, and Puhlmann F Mouratidis H and Rolland C Process model generation from natural language text Advanced Information Systems Engineering 2011 Heidelberg Springer 482-496

[16]

Hershcovich, D., Abend, O., Rappoport, A.: A transition-based directed acyclic graph parser for UCCA. In: Proceedings of the ACL. ACL (2017)

[17]

Jia, R., Liang, P.: Data recombination for neural semantic parsing. In: Proceedings of ACL. ACL (2016)

[18]

Jlailaty D, Grigori D, and Belhajjame K Kotzinos D, Laurent D, Spyratos N, Tanaka Y, and Taniguchi R Email business activities extraction and annotation Information Search, Integration, and Personalization 2019 Cham Springer 69-86

[19]

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)

[20]

Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of ICLR (2017)

[21]

Körner SJ and Landhäußer M Hopfe CJ, Rezgui Y, Métais E, Preece A, and Li H Semantic enriching of natural language texts with automatic thematic role annotation Natural Language Processing and Information Systems 2010 Heidelberg Springer 92-99

[22]

Leopold H, van der Aa H, and Reijers HA Gulden J, Reinhartz-Berger I, Schmidt R, Guerreiro S, Guédria W, and Bera P Identifying candidate tasks for robotic process automation in textual process descriptions Enterprise, Business-Process and Information Systems Modeling 2018 Cham Springer 67-81

[23]

López, H.A., Debois, S., Hildebrandt, T.T., Marquard, M.: The process highlighter: from texts to declarative processes and back. In: CEUR Workshop Proceedings (2018)

[24]

Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR, Workshop Track Proceedings (2013)

[25]

Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the Conference on EMNLP (2014)

[26]

Qian C et al. Dustdar S, Yu E, Salinesi C, Rieu D, Pant V, et al. An approach for process model extraction by multi-grained text classification Advanced Information Systems Engineering 2020 Cham Springer 268-282

[27]

Quishpi L, Carmona J, and Padró L Fahland D, Ghidini C, Becker J, and Dumas M Extracting annotations from textual descriptions of processes Business Process Management 2020 Cham Springer 184-201

[28]

Schlichtkrull M, Kipf TN, Bloem P, van den Berg R, Titov I, Welling M, et al. Gangemi A et al. Modeling relational data with graph convolutional networks The Semantic Web 2018 Cham Springer 593-607

[29]

Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. In: IEEE SPM (2013)

[30]

Straka, M., Straková, J.: Tokenizing, POS tagging, lemmatizing and parsing UD 2.0 with UDPipe. In: Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (2017)

[31]

Sànchez-Ferreres J, Burattin A, Carmona J, Montali M, and Padró L Hildebrandt T, van Dongen BF, Röglinger M, and Mendling J Formal reasoning on natural language descriptions of processes Business Process Management 2019 Cham Springer 86-101

[32]

Tsai, R.T.H., et al.: Various criteria in the evaluation of biomedical named entity recognition. BMC Bioinform. 7, 92 (2006)

[33]

Wang, M., et al.: Deep graph library: a graph-centric, highly-performant package for graph neural networks. arXiv: Learning (2019)

[34]

Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. In: IEEE Transactions on NNLS (2020)

[35]

Zhang, Z., Sabuncu, M.: Generalized cross entropy loss for training deep neural networks with noisy labels. In: NeurIPS (2018)

Cited By

Kapugama Geeganage DWynn Mter Hofstede A(2024)Text2EL+: Expert Guided Event Log Enrichment Using Unstructured TextJournal of Data and Information Quality10.1145/364001816:1(1-28)Online publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1145/3640018
Neuberger JAckermann Lvan der Aa HJablonski S(2024)A Universal Prompting Strategy for Extracting Process Model Information from Natural Language Text Using Large Language ModelsConceptual Modeling10.1007/978-3-031-75872-0_3(38-55)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1007/978-3-031-75872-0_3
Neuberger JAckermann LJablonski S(2023)Beyond Rule-Based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language TextCooperative Information Systems10.1007/978-3-031-46846-9_10(179-197)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-46846-9_10

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Data-Driven Annotation of Textual Process Descriptions Based on Formal Meaning Representations

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Information & Contributors

Information

Published In

cover image Guide Proceedings

Advanced Information Systems Engineering: 33rd International Conference, CAiSE 2021, Melbourne, VIC, Australia, June 28 – July 2, 2021, Proceedings

Jun 2021

576 pages

ISBN:978-3-030-79381-4

DOI:10.1007/978-3-030-79382-1

Editors:
Marcello La Rosa
The University of Melbourne, Melbourne, VIC, Australia
,
Shazia Sadiq
The University of Queensland, St Lucia, QLD, Australia
,
Ernest Teniente
Universitat Politècnica de Catalunya, Barcelona, Spain

© Springer Nature Switzerland AG 2021.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 28 June 2021

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Cited By

Kapugama Geeganage DWynn Mter Hofstede A(2024)Text2EL+: Expert Guided Event Log Enrichment Using Unstructured TextJournal of Data and Information Quality10.1145/364001816:1(1-28)Online publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1145/3640018
Neuberger JAckermann Lvan der Aa HJablonski S(2024)A Universal Prompting Strategy for Extracting Process Model Information from Natural Language Text Using Large Language ModelsConceptual Modeling10.1007/978-3-031-75872-0_3(38-55)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1007/978-3-031-75872-0_3
Neuberger JAckermann LJablonski S(2023)Beyond Rule-Based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language TextCooperative Information Systems10.1007/978-3-031-46846-9_10(179-197)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-46846-9_10

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