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== SDMX Implementation == |
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-International standards like Statistical Data and Metadata eXchange ([[SDMX>>https://sdmx.org/]]) have provided a robust foundation for metadata exchange in official statistics. However, our experience has revealed significant limitations influencing the achievement of semantic interoperability. SKMS addresses these gaps by integrating SDMX structures into a semantic interpretation environment via the Interoperability Basis platform. The platform supports semantic alignment, enrichment, and publication of data exchange standards using a knowledge management system, modeling tools, namespace control, and persistent URI infrastructure. |
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+International standards like Statistical Data and Metadata eXchange ([[SDMX>>https://sdmx.org/||rel="noopener noreferrer" target="_blank"]]) have provided a robust foundation for metadata exchange in official statistics. However, our experience has revealed significant limitations influencing the achievement of semantic interoperability. SKMS addresses these gaps by integrating SDMX structures into a semantic interpretation environment via the Interoperability Basis platform. The platform supports semantic alignment, enrichment, and publication of data exchange standards using a knowledge management system, modeling tools, namespace control, and persistent URI infrastructure. |
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== Linked Data == |
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Another priority of HLG-MOS is the development of rich (“[[smart>>http://cosmos-conference.org/index.html||rel="noopener noreferrer" target="_blank"]]”) metadata — metadata that is standardised (understandable and reusable across contexts), active (capable of driving statistical processes), and aligned with the [[FAIR principles>>https://www.go-fair.org/||rel="noopener noreferrer" target="_blank"]] : Findable, Accessible, Interoperable, and Reusable. |
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-We share these goals and move forward in step with HLG-MOS initiatives — SKMS already reflects key principles and objectives that resonate with this international agenda. |
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+We share these goals and move forward in step with [[HLG-MOS>>https://unece.org/statistics/networks-of-experts/high-level-group-modernisation-statistical-production-and-services||rel="noopener noreferrer" target="_blank"]] initiatives — SKMS already reflects key principles and objectives that resonate with this international agenda. |
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A key enabler of [[FAIR>>https://www.go-fair.org/||rel="noopener noreferrer" target="_blank"]] implementation in statistics is the use of semantic technologies for both data dissemination and the formalization of knowledge in the form of semantic models (semantic assets). Semantic assets (SAs) are reusable formal representations of data such as: (1) metadata schemas (e.g. XML or RDF), (2) core data models or common models, (3) ontologies, thesauri, and reference data (e.g. code lists, taxonomies, glossaries). These assets are published as open data standards and used in the development of knowledge management systems, harmonizing indicators and classifications, and preparing LOSD. Semantic models support unambiguous interpretation, semantic search, and the discovery of data across disparate sources. |
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