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Towards a Knowledge Graph for Models and Algorithms in Applied Mathematics

  • Mathematical models and algorithms are an essential part of mathematical research data, as they are epistemically grounding numerical data. To make this research data FAIR, we present how two previously distinct ontologies, MathAlgoDB for algorithms and MathModDB for models, were merged and extended into a living knowledge graph as the key outcome. This was achieved by connecting the ontologies through computational tasks that correspond to algorithmic tasks. Moreover, we show how models and algorithms can be enriched with subject-specific metadata, such as matrix symmetry or model linearity, essential for defining workflows and determining suitable algorithms. Additionally, we propose controlled vocabularies to be added, along with a new class that differentiates base quantities from specific use case quantities. We illustrate the capabilities of the developed knowledge graph using two detailed examples from different application areas of applied mathematics, having already integrated over 250 research assets into the knowledge graph.

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Metadaten
Author:Björn SchemberaORCiD, Frank WübbelingORCiD, Hendrik KleikampORCiD, Burkhard SchmidtORCiD, Aurela ShehuORCiD, Marco ReidelbachORCiD, Christine BiedingerORCiD, Jochen FiedlerORCiD, Thomas KopruckiORCiD, Dorothea IglezakisORCiD, Dominik GöddekeORCiD
Document Type:In Proceedings
Parent Title (English):Metadata and Semantic Research. MTSR 2024
Volume:2331
First Page:95
Last Page:109
Series:Communications in Computer and Information Science
Publisher:Springer Nature Switzerland
Place of publication:Cham
Year of first publication:2025
Page Number:15
ISSN:1865-0929
DOI:https://doi.org/10.1007/978-3-031-81974-2_8
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