Ontology-driven Data Curation and Knowledge Graphs for Catalyst Layers in PEM Fuel Cells

This poster presents an ontology-driven framework for data curation and knowledge graph construction for catalyst layers in PEM fuel cells. The workflow integrates structured metadata extraction, semantic harmonization, and graph-based representation of electrochemical measurements and material relationships to support FAIR and interoperable hydrogen research data.

The presented approach combines ontology-guided information extraction, heterogeneous data integration, and Neo4j-based knowledge graph representation to connect electrochemical methods, material properties, and experimental conditions across scientific literature. The framework supports scalable and machine-interpretable representation of catalyst layer research knowledge.

The work is developed within the AIMWORKS project under the Helmholtz Metadata Collaboration (HMC) initiative.