| Autor: |
Anna Teern, Markus Kelanti, Tero Päivärinta, Mika Karaila |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Complex Systems Informatics and Modeling Quarterly, Vol 0, Iss 36, Pp 1-15 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2255-9922 |
| DOI: |
10.7250/csimq.2023-36.01 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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