Automatically annotating business process models with ontology concepts at design-time

Autor(en): Riehle, D.M.
Jannaber, S.
Delfmann, P.
Thomas, O. 
Becker, J.
Herausgeber: de Cesare, S.
Frank, U.
Stichwörter: Analysis; Automatic annotation; Big data; Business process model; Business process modelling; Data mining; Domain-specific ontologies; Language independents; Ontology; Process model; Process model qualities, Quality control; Process models; Systems engineering, Analysis
Erscheinungsdatum: 2017
Herausgeber: Springer Verlag
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen: 10651 LNCS
Startseite: 177
Seitenende: 186
Zusammenfassung: 
In business process modelling, it is known that using a consistent labelling style and vocabulary improves process model quality. In this regard, several existing approaches aim at the linguistic support for labelling model elements. At the same time, domain-specific ontologies have been proposed and used to capture important process-related knowledge. However, these two areas are largely disconnected up to now. Although some research suggests annotating ontology concepts to process models, for instance, to interpret and reason about a process model, annotation has not yet gained traction in practice since it still has to be done in a highly manual effort. We thus provide an automated, language-independent methodology for using labelling assistance functionalities to identify and annotate relevant ontology concepts to process model elements using a four-step procedural model. © 2017, Springer International Publishing AG.
Beschreibung: 
Conference of 36th International Conference on Conceptual Modeling, ER 2017 held in Conjuction with the 3rd International Workshop on Modeling for Ambient Assistance and Healthy Ageing, AHA 2017, 6th International Workshop on Modeling and Management of Big Data, MoBiD 2017, 4th International Workshop on Conceptual Modeling in Requirements and Business Analysis, MREBA 2017, 5th International Workshop on Ontologies and Conceptual Modeling, OntoCom 2017 and 4th Workshop on Quality of Models and Models of Quality, QMMQ 2017 ; Conference Date: 6 November 2017 Through 9 November 2017; Conference Code:203819
ISBN: 9783319706245
ISSN: 03029743
DOI: 10.1007/978-3-319-70625-2_17
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034214499&doi=10.1007%2f978-3-319-70625-2_17&partnerID=40&md5=99d85ceb356ca0bb3262ac660d543fd5

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