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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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-The adoption of LOSD creates new opportunities for discovering, searching, comparing, and integrating statistical data from multiple sources through [[Semantic Web>>https://www.w3.org/standards/||rel="noopener noreferrer" target="_blank"]] technologies, including semantic integration methods. This approach enables the achievement of the highest level of data maturity according to the [[5-star model>>https://5stardata.info/en/]] proposed by Tim Berners-Lee. |
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+The adoption of LOSD creates new opportunities for discovering, searching, comparing, and integrating statistical data from multiple sources through [[Semantic Web>>https://www.w3.org/standards/||rel="noopener noreferrer" target="_blank"]] technologies, including semantic integration methods. This approach enables the achievement of the highest level of data maturity according to the [[5-star model>>https://5stardata.info/en/||target="_blank"]] proposed by Tim Berners-Lee. |
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== Operational Cycle == |
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1. The development of [[glossaries>>doc:working:Glossary.WebHome||target="_blank"]] (the formation of detailed terminological articles), indicators descriptions based on the analysis of methodological documents, and then the generation of corresponding semantic assets. Refinement of hyper-text markup in accordance with modelled glossaries. |
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1. Publishing semantic assets generated in the SKMS. |
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1. Development, aligning and cataloging of necessary SA, code lists or other models of statistical domains in accordance with semantic standards. |
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-1. Importing datasets from external sources or data warehouses (DWH). Transformation of datasets using the [[RDF Data Cube Vocabulary>>https://www.w3.org/TR/vocab-data-cube/]], semantic enrichment. |
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+1. Importing datasets from external sources or data warehouses (DWH). Transformation of datasets using the [[RDF Data Cube Vocabulary>>https://www.w3.org/TR/vocab-data-cube/||target="_blank"]], semantic enrichment. |
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1. Visualization and validation of semantic models and LOSD sets. |
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1. Construction of rich metadata that is transmitted for publishing in external analytical systems. |
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-SKMS is based on the XWiki extension to using semantic technologies. It provides special templates for publishing documents, glossary terms, and indicator descriptions. They are used by domain experts to formalize statistical knowledge and provide their human-readable representation fixed in SAs. The LOSD pipeline is supported by generators and constructors developed to automate the formation of LOSD, semantic models and semantically enriched metadata. SKMS may be integrated with a cataloging service that supports not only the organisation of semantic assets, but also their visualization, access, and dissemination through standard interfaces such as [[OpenAPI>>https://www.openapis.org/]] and [[SPARQL Endpoints>>https://sparql.dev/article/SPARQL_endpoints_and_how_to_use_them.html]]. |
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+SKMS is based on the XWiki extension to using semantic technologies. It provides special templates for publishing documents, glossary terms, and indicator descriptions. They are used by domain experts to formalize statistical knowledge and provide their human-readable representation fixed in SAs. The LOSD pipeline is supported by generators and constructors developed to automate the formation of LOSD, semantic models and semantically enriched metadata. SKMS may be integrated with a cataloging service that supports not only the organisation of semantic assets, but also their visualization, access, and dissemination through standard interfaces such as [[OpenAPI>>https://www.openapis.org/||target="_blank"]] and [[SPARQL Endpoints>>https://sparql.dev/article/SPARQL_endpoints_and_how_to_use_them.html||target="_blank"]]. |
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== Benefits == |
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