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13 13  {{box cssClass="box_green"}}
14 14  == SDMX Implementation ==
15 15  
16 -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>>https://basis.semanticip.org/xwiki/bin/view/Main/||target="_blank"]]. The platform supports semantic alignment, enrichment, and publication of data exchange standards using a knowledge management system, modeling tools, namespace control, and persistent [[URI>>https://www.w3.org/Addressing/URL/uri-spec.html||rel="noopener noreferrer" target="_blank"]] infrastructure.
16 +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>>https://basis.semanticip.org/xwiki/bin/view/Main/||rel="noopener noreferrer" target="_blank"]]. The platform supports semantic alignment, enrichment, and publication of data exchange standards using a knowledge management system, modeling tools, namespace control, and persistent [[URI>>https://www.w3.org/Addressing/URL/uri-spec.html||rel="noopener noreferrer" target="_blank"]] infrastructure.
17 17  {{/box}}
18 18  
19 19  == Linked Data ==
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26 26  
27 27  The High-Level Group for the Modernisation of Official Statistics ([[HLG-MOS>>https://unece.org/statistics/networks-of-experts/high-level-group-modernisation-statistical-production-and-services||rel="noopener noreferrer" target="_blank"]]), under the United Nations Economic Commission for Europe ([[UNECE>>https://unece.org/ru||rel="noopener noreferrer" target="_blank"]]), addresses the challenges of data interoperability within national statistical systems. It develops and promotes methods, models (including semantic models such as ontologies), and standards through coordinated initiatives. One of these initiatives is the Data Governance Framework for Statistical Interoperability ([[DAFI>>https://unece.org/sites/default/files/2024-03/HLG2023%20DAFI%20Final_0.pdf]]), published in 2023. This framework provides a reference model for implementing governance programs that support the creation, sharing, and use of data in ways that preserve semantic meaning across systems.
28 28  
29 -Another priority of [[HLG-MOS>>https://unece.org/statistics/networks-of-experts/high-level-group-modernisation-statistical-production-and-services||rel="noopener noreferrer" target="_blank"]] is the development of [[rich (“smart”) metadata>>http://cosmos-conference.org/index.html]] — 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.
29 +Another priority of [[HLG-MOS>>https://unece.org/statistics/networks-of-experts/high-level-group-modernisation-statistical-production-and-services||rel="noopener noreferrer" target="_blank"]] is the development of [[rich (“smart”) metadata>>http://cosmos-conference.org/index.html||rel="noopener noreferrer" target="_blank"]] — 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.
30 30  
31 31  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.
32 32  
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40 40  
41 41  The full operational cycle consists of seven stages:
42 42  
43 -1. Collection and systematization of methodological documents (creation of an electronic library), adding annotations, discovering terms-candidates and primary markup with related terms and documents. Publishing documents in original structured form with hypertext markup in a specialized "Methodology" section.
44 -1. The development of glossaries (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.
43 +1. Collection and systematization of methodological documents (creation of an electronic [[library>>doc:working:Library.WebHome||target="_blank"]]), adding annotations, discovering terms-candidates and primary markup with related terms and documents. Publishing documents in original structured form with hypertext markup in a specialized "[[Methodology>>doc:working:Methodology.WebHome||target="_blank"]]" section.
44 +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.
45 45  1. Publishing semantic assets generated in the SKMS.
46 46  1. Development, aligning and cataloging of necessary SA, code lists or other models of statistical domains in accordance with semantic standards.
47 47  1. Importing datasets from external sources or data warehouses (DWH). Transformation of datasets using the RDF Data Cube Vocabulary, semantic enrichment.