FAIR and Structured Data: A Domain Ontology Aligned with Standard-Compliant Tensile Testing
Markus Schilling, Bernd Bayerlein, Philipp von Hartrott, Jörg Waitelonis, Henk Birkholz, Pedro Dolabella Portella, Birgit Skrotzki
Advanced Engineering Materials, 2024
doi: 10.1002/adem.202400138
discovery-gemini-llm-reviewed-20260524
The unsustainable and conditionally reusable way of storing and handling data about materials has been identified by the materials science and engineering (MSE) community as a major constraint for qualitative growth in research and product design. This paper
MoreLess
presents the development of a domain ontology specifically aligned with standard-compliant tensile testing, created within the framework of the Platform MaterialDigital (PMD) initiative. The publication offers insights into the development process of domain ontologies, using the tensile test as a primary example. It demonstrates how semantic technologies and the FAIR (Findable, Accessible, Interoperable, and Reusable) principles can be applied to explore and find the best digitization approaches for materials data handling. By achieving semantic interoperability across different material domains, the ontology enables a structured, machine-readable representation of experimental mechanical testing data, facilitating better data integration and reuse in industrial data spaces.
M. Schilling, B. Bayerlein, P. v. Hartrott, J. Waitelonis, H. Birkholz, P. D. Portella, B. Skrotzki, "Tensile Test Ontology (TTO): A Semantic Layer for Unified Storage and Retrieval of Tensile Test Data", Advanced Engineering Materials, 2024, 2400138.
Added by matportal-botMay 24, 2026
Digitalizing Material Knowledge: A Practical Framework for Ontology-Driven Knowledge Graphs in Process Chains
Elena Garcia Trelles, Christoph Schweizer, Akhil Thomas, Philipp von Hartrott, Marina Janka-Ramm
Applied Sciences, 2024
doi: 10.3390/app142411683
discovery-gemini-llm-reviewed-20260524
This paper proposes a robust methodology for integrating process-specific data and domain expert knowledge into linked knowledge graphs. These graphs utilize an ontology that provides a standardized vocabulary for material science and facilitates the creation
MoreLess
of semantic models for various processes along the manufacturing chain. The framework establishes a scalable semantic basis for integrating ontology-driven knowledge representation into advanced decision support systems. By digitalizing material data through semantic modeling, machine-readable data with contextual metadata is stored in a graph database. This data can be efficiently queried using the SPARQL language, enabling seamless integration into data pipelines and providing a foundation for subsequent integration into explainable engineering systems and model-based engineering environments.
Elena Garcia Trelles, Christoph Schweizer, Akhil Thomas, Philipp von Hartrott, Marina Janka-Ramm. (2024). Digitalizing Material Knowledge: A Practical Framework for Ontology-Driven Knowledge Graphs in Process Chains. Applied Sciences. doi:10.3390/app142411683
Added by matportal-botMay 24, 2026
Repositories
1
Repositorygitlab.cc-asp.fraunhofer.de
EMI_datamanagement / bwmd_ontology
The primary repository for the BWMD ontology hosted on the Fraunhofer GitLab instance.
Sign in to MatPortal and configure your own usable provider credentials or supported Codex or Gemini Antigravity account before sending an Assistant request.
Page Context:
Current Page
Loading context...
How to use this Assistant
Choose a quick action or type a custom question in the input box below.
The "Page Context" card above displays the active page metadata that is forwarded to the AI.
For SPARQL, you can click the "Insert into SPARQL Editor" button on code blocks to automatically load queries into the editor.
All actions are strictly proposal-only; the assistant will never perform writes or mutations on your behalf.
How can I help you on this page? Choose a quick action or type a question below.