University of Oulu

Teern, A., Kelanti, M., Päivärinta, T., & Karaila, M. (2023). Design objectives for evolvable knowledge graphs. Complex Systems Informatics and Modeling Quarterly, 36, 197.

Design objectives for evolvable knowledge graphs

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Author: Teern, Anna1; Kelanti, Markus1; Päivärinta, Tero1,2;
Organizations: 1M3S, University of Oulu, Oulu, 90014, Finland
2Information Systems, Luleå University of Technology, Luleå, 97187, Sweden
3Valmet Automation Systems, Valmet Oyj, Tampere, 33900, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
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Language: English
Published: Riga Technical University, 2023
Publish Date: 2023-11-14


Knowledge graphs (KGs) structure knowledge to enable the development of intelligent systems across several application domains. In industrial maintenance, comprehensive knowledge of the factory, machinery, and components is indispensable. This study defines the objectives for evolvable KGs, building upon our prior research, where we initially identified the problem in industrial maintenance. Our contributions include two main aspects: firstly, the categorization of learning within the KG construction process and the identification of design objectives for the KG process focusing on supporting industrial maintenance. The categorization highlights the specific requirements for KG design, emphasizing the importance of planning for maintenance and reuse.

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Series: Complex systems informatics and modeling quarterly
ISSN: 2255-9922
ISSN-E: 2255-9922
ISSN-L: 2255-9922
Issue: 197
Article number: 197
DOI: 10.7250/csimq.2023-36.01
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This research has been partially funded by the ITEA project Oxilate (18023) and Business Finland.
Copyright information: © 2023 Anna Teern, Markus Kelanti, Tero Päivärinta, and Mika Karaila. This is an open access article licensed under the Creative Commons Attribution License (