Record link: 
http://hdl.handle.net/20.500.12708/188264
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Title: 
Exploring Enterprise Architecture Knowledge Graphs in Archi: The EAKG Toolkit
en
Citation: 
Glaser, P.-L., Ali, S. J., Sallinger, E., & Bork, D. (2023). Exploring Enterprise Architecture Knowledge Graphs in Archi: The EAKG Toolkit. In Conference Proceedings: Enterprise Design, Operations, and Computing. EDOC 2022 Workshops (pp. 332–338). Springer. https://doi.org/10.1007/978-3-031-26886-1_21
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Publisher DOI: 
10.1007/978-3-031-26886-1_21
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Publication Type: 
Inproceedings - Poster Contribution
en
 
Konferenzbeitrag - Poster Contribution
de
Language: 
English
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Authors: 
Glaser, Philipp-Lorenz 
Ali, Syed Juned 
Sallinger, Emanuel 
Bork, Dominik 
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Organisational Unit: 
E194-03 - Forschungsbereich Business Informatics 
E194 - Institut für Information Systems Engineering 
E192 - Institut für Logic and Computation 
E192-02 - Forschungsbereich Databases and Artificial Intelligence 
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Published in: 
Conference Proceedings: Enterprise Design, Operations, and Computing. EDOC 2022 Workshops 
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ISBN: 
978-3-031-26886-1
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Volume: 
466
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DOI of the book: 
10.1007/978-3-031-26886-1
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Date (published): 
2023
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Event name: 
Enterprise Design, Operations, and Computing. EDOC 2022 Workshops 
en
Event date: 
4-Oct-2022 - 7-Oct-2022
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Event place: 
Bozen-Bolzano, Italy
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Number of Pages: 
7
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Publisher: 
Springer, Cham
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Peer reviewed: 
Yes
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Keywords: 
Archi; ArchiMate; Enterprise architecture; Knowledge graph; Modeling tool
en
Abstract: 
This paper presents the EAKG Toolkit that entails a new Knowledge Graph-based representation of enterprise architecture (EA) models and further enables reasoning on EA knowledge. Our developed EAKG Toolkit is unique in the sense that it i) transforms ArchiMate models into a KG representation – the Enterprise Architecture Knowledge Graph (EAKG), ii) visualizes the EAKG for interactive exploration, and iii) extends the EAKG with additional nodes and edges to visually represent detected EA smells.
en
Research facilities: 
Pilotfabrik 
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Project title: 
MFP 4.2 Advanced Analytics for Smart Manufacturing: 854187 (CDP Center for Digital Production G) 
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Project (external): 
Erasmus+
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Project ID: 
2021-1-RO01-KA220- HED-000027576
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Research Areas: 
Information Systems Engineering: 100%
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Science Branch: 
1020 - Informatik: 90%
5020 - Wirtschaftswissenschaften: 10%
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Appears in Collections:
Conference Paper

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