Semantic Integration of Data in Materials Science

This research article presents a study on the application of Semantic Web technologies to enhance Materials Science and Engineering (MSE) through the integration of diverse datasets. The focus is on a 2000 series age-hardenable aluminum alloy, where mechanical and microstructural properties are correlated using tensile tests and dark-field transmission electron microscopy. An expandable knowledge graph is constructed utilizing the Tensile Test and Precipitate Geometry Ontologies aligned with the PMD Core Ontology, facilitating data integration while adhering to FAIR principles. The study develops a Jupyter Notebook demonstrator that illustrates the semantic integration process, employing the Orowan mechanism as a use case. Key focal points include the methodical aggregation of datasets, development of an ontological framework, and the creation of a queryable knowledge graph. The results highlight the interdependence of mechanical and microstructural properties, demonstrating the potential for enhanced analytical capabilities in MSE through semantic data integration.