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

Title data

Ackermann, Lars ; Neuberger, Julian ; Jablonski, Stefan:
Data-Driven Annotation of Textual Process Descriptions Based on Formal Meaning Representations.
In: la Rosa, Marcello ; Sadiq, Shazia ; Teniente, Ernest (ed.): Advanced Information Systems Engineering : CAiSE 2021, Proceedings. - Cham : Springer , 2021 . - pp. 75-90 . - (Lecture Notes in Computer Science ; 12751 ) (Information Systems and Applications, incl. Internet/Web, and HCI ; 12751)
ISBN 978-3-030-79382-1
DOI: https://doi.org/10.1007/978-3-030-79382-1_5

Official URL:

Abstract in another language

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.

Further data

Item Type:	Article in a book
Refereed:	Yes
Keywords:	Process Modeling; Text Annotation; Semantic Parsing; Graph convolutional networks
Institutions of the University:	Faculties > Faculty of Mathematics, Physics und Computer Science Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV > Chair Applied Computer Science IV - Univ.-Prof. Dr.-Ing. Stefan Jablonski Faculties
Result of work at the UBT:	Yes
DDC Subjects:	000 Computer Science, information, general works > 004 Computer science
Date Deposited:	11 Mar 2022 08:54
Last Modified:	12 Jul 2023 13:49
URI:	https://eref.uni-bayreuth.de/id/eprint/68901