Klaassen, C., Thoma, M. A., Steindl, G., Amiri, A., Kasper, L., & Hofmann, R. (2024). Semantic Annotation of System Models for Generating RDF Runtime Models. In 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN) (pp. 1–6). IEEE. https://doi.org/10.1109/INDIN58382.2024.10774513
2024 IEEE 22nd International Conference on Industrial Informatics (INDIN)
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Event date:
18-Aug-2024 - 20-Aug-2024
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Event place:
Peking, China
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Number of Pages:
6
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Publisher:
IEEE
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Peer reviewed:
Yes
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Keywords:
Energy system modeling; SysML v2; Semantic Web; Ontologies; Model Transformation
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Abstract:
The paper addresses the challenge of efficiently and reliably transforming system models into semantically enriched runtime models by integrating ontologies to preserve semantic integrity. The approach leverages domain specialists to annotate system models with semantic tags, bridging expert perspectives and creating a unified, comprehensive representation of the system. Manually created runtime models tend to be inconsistent with the original system model. Moreover, the process is time-consuming and does not scale well for larger models. Therefore, a tool-assisted workflow is proposed that offers support at every step, from annotating the system model to generating the semantically enriched runtime model. This approach allows using definitions from semantic web ontologies in system models as tags. These semantic annotations are then used in the generation of the system’s runtime model. The workflow is showcased by applying it to a system model of a Packed Bed Thermal Energy Storage (PBTES).
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Research Areas:
Computer Engineering and Software-Intensive Systems: 20% Information Systems Engineering: 30% Modeling and Simulation: 50%