PMD Core Ontology (PMDCO)

representation. Ontologies have emerged as powerful tools for establishing interoperable and reusable data structures from inconsistent data structures. Despite advancements in semantic data representation for specific applications, integrating application ontologies with primary data repositories, such as electronic lab notebooks (ELNs), to feed world data remains an open task. As a use case in the MSE domain, this work presents a system based on semantic technologies from the point of view of engineers, developed with the help of information scientists, and unraveled on a small scale. The development of an application ontology (AO) was elaborated for flame spray pyrolysis (FSP) processes with the implementation of a data pipeline. The proposed FSP application ontology emerges from experimental in-house best-practice procedures and is adapted to the mid-level Project Material Digital core ontology (PMDco) to allow interoperability within the MSE domain. The pipeline retrieves manually acquired experimental data from an ELN, translates it into a machine-actionable format, and converts it into a Resource Description Framework (RDF) format to support semantic interoperability. The latter was stored in a triple store with a SPARQL interface, enabling findable and accessible datasets that are searchable and traceable. By creating semantically linked data structures in line with FAIR principles, this approach allows traceable and findable experimental results between stakeholders through both human-readable and machine-actionable formats. Seamless integration of the modular microservices of the data pipeline within established lab practices minimizes disruption while maintaining the software framework. The present work demonstrates the practical implementation of a FAIR data pipeline within a laboratory setting, paving the way for future data-centric science.

In particular, Block 2 highlights how PMDco structures core domain concepts—such as materials, processes, and properties—and connects them through reusable design patterns to support consistent data modeling. This enables the integration of heterogeneous experimental and computational data into interoperable knowledge graphs, ultimately facilitating FAIR data principles and improving reproducibility and collaboration across materials science workflows.

of measurement hinder effective collaboration and large-scale analysis. To address these barriers, this research introduces a novel methodology that exploits Large Language Models to semantically align two key ontologies: the Platform Material Digital Core Ontology (PMDco), which describes experimental methodologies and material measurements, and the Quantities, Units, Dimensions and Types Ontology (QUDT), which standardizes units and quantities. Our methodology systematically aligns PMDco classes such as those quantifying mass loss, corrosion depth, or corrosion rate to precise QUDT units by assessing semantic labels, structural context, and physical relevance using LLM-generated interpretations. The resulting OWL-based semantic mappings enable automated recognition and normalization of measurement data across heterogeneous sources, facilitating consistent interpretation despite variations in units or test protocols. The significance of this ontology alignment lies in its ability to substantially improve semantic interoperability within corrosion datasets, thereby reducing ambiguity, preventing misinterpretation, and enabling automated reasoning. Domain experts benefit directly from more efficient cross-laboratory data comparisons, enhanced data quality, and strengthened foundations for scalable materials informatics infrastructures. Ultimately, this approach supports FAIR (Findable, Accessible, Interoperable, Reusable) data principles, advancing digital transformation in corrosion research and material science.

from tensile tests and dark-field transmission electron microscopy across varied aging times. An expandable knowledge graph, constructed using the Tensile Test and Precipitate Geometry Ontologies aligned with the PMD Core Ontology, facilitates this integration. This approach adheres to FAIR principles and enables sophisticated analysis via SPARQL queries, revealing correlations consistent with the Orowan mechanism. The study highlights the potential of semantic data integration in MSE, offering a new approach for data-centric research and enhanced analytical capabilities.

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.

to align cross-domain. Generic top-level semantic terminology often is too abstract, while MSE domain terminology often is too specific. In this paper, an approach how to maintain a comprehensive MSE-centric terminology composing a mid-level ontology–the Platform MaterialDigital Core Ontology (PMDco)–via MSE community-based curation procedures is presented. The illustrated findings show how the PMDco bridges semantic gaps between high-level, MSE-specific, and other science domain semantics. Additionally, it demonstrates how the PMDco lowers development and integration thresholds. Moreover, the research highlights how to fuel it with real-world data sources ranging from manually conducted experiments and simulations with continuously automated industrial applications.