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  4. Towards a Knowledge Graph for Models and Algorithms in Applied Mathematics
 
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2025
Conference Paper
Title

Towards a Knowledge Graph for Models and Algorithms in Applied Mathematics

Abstract
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.
Author(s)
Schembera, Björn
Wübbeling, Frank
Kleikamp, Hendrik
Schmidt, Burkhard
Shehu, Aurela
Reidelbach, Marco
Biedinger, Christine  
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM  
Fiedler, Jochen
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM  
Koprucki, Thomas
Iglezakis, Dorothea
Göddeke, Dominik
Mainwork
Metadata and Semantic Research. 18th Research Conference, MTSR 2024  
Conference
International Metadata and Semantics Research Conference 2024  
Open Access
DOI
10.1007/978-3-031-81974-2_8
Additional full text version
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Language
English
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM  
Keyword(s)
  • Mathematical models

  • MathAlgoDB

  • MathModDB

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