Towards FAIR and Interoperable Statistical Data

The Semantic Knowledge Management System (SKMS) is a platform for structuring and publishing institutional statistical knowledge using Semantic Web technologies. It transforms internal expertise, documents, and metadata into a coherent, machine-interpretable semantic environment.

Statistical glossaries, classifications, and indicators are extracted, formalised, and semantically enriched through the analysis of materials collected in the document Library. These assets are then represented as semantic models (RDF, SKOS, XKOS), with persistent URIs and alignment across institutions, and serve as the foundation for the preparation and dissemination of linked open statistical data and smart metadata.

For SDMX-based structures, SKMS integrates with the Interoperability Basis platform, enabling the semantic transformation and publication of SDMX concepts, code lists, and data structures in accordance with Linked Data principles.

SKMS fosters the discovery, reuse, and interoperability of statistical data and metadata, providing a robust foundation for implementing the FAIR principles across organizational and national boundaries.

We invite partners to join the international, non-profit Interoperability Basis initiative and a shared semantic infrastructure

• International organizations. Can benefit from using a sustainable platform for expressing their standards as semantic assets: transform, enrich and align their structures and semantics; apply tools to convert/develop data schemas, vocabularies, and code lists into RDF/OWL; assign persistent URIs; manage the sections of their standards in the common namespace; maintain these discoverable, reusable, and interoperable assets across domains.
• Governments and public administrations. Can release high-value datasets and classification systems based on semantically transformed, enriched and aligned standards provided by the platform. They can also benefit from sustainable publishing of public data using persistent URIs, semantically enhanced classifications, and interoperable APIs, while also aligning their open data with semantic standards to ensure long-term transparency and cross-border interoperability.
• Enterprises. Can leverage the platform as a foundation for building a corporate semantic core and for managing enterprise metadata in a consistent, standards-based way. This shared semantic infrastructure accelerates information sharing, supports digital transformation, and enables the development of AI applications that rely on high-quality, interoperable data. The platform’s persistent URIs and standardized semantic models help reduce implementation costs for scalable enterprise solutions.
• Academic and research institutions. Can benefit from an open, authoritative interoperability infrastructure that fosters international collaboration across research teams and disciplines. The platform supports joint pilot projects, semantic modeling studies, and contributions to the development and alignment of data standards, enabling researchers to actively participate in shaping the semantic foundations of global data exchange.
• Semantic Web technology developers. Can use the platform’s namespace to build and publish their own semantic assets with persistent URIs. Participation enables integration with real-world data ecosystems, early access to new semantic standards, and visibility within a community working on applied Linked Data solutions.

Together, we can drive the global transition towards FAIR and interoperable data through semantics — fostering the growth of Linked Data and enabling smarter, AI-driven applications powered by clear, reusable, and semantically rich data models.