Leveraging Ontologies and Reasoning for FAIR Data in ESRF Experiments

We demonstrate that semantic modeling with ontologies provides a robust and enduring approach to achieving FAIR data in our experimental environment. By endowing data with self‑describing semantics through ontological definitions and inference, we enable them to ‘speak’ for themselves. Building on PaNET, we define techniques in ESRFET by their characteristic building blocks. The outcome is a standards‑based framework (RDF, OWL, SWRL, SPARQL, SHACL) that encodes experimental techniques’ semantics and underpins a broader facility ontology. Our approach illustrates that by using differential definitions, semantic enrichment through linking to multiple ontologies, and documented semantic negotiation, we standardize experimental techniques' descriptions and annotations—ensuring enhanced discoverability, reproducibility, and integration within the FAIR data ecosystem. This talk was held in the course of the DAPHNE4NFDI TA1 Data for science lecture series on April, 29 2025