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edited by Helena
on 2025/06/26 15:54
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To version 20.1
edited by Elena
on 2025/06/26 16:33
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1 -XWiki.helena
1 +XWiki.elenasemanticproorg
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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. 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/]]. 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 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.
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>>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.
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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73 73  Each user group contributes to and benefits from the semantic foundation provided by SKMS:
74 74  
75 75  * **National Statistical Offices**: expected to provide domain-specific documentation, develop national semantic assets, and integrate LOSD into their official dissemination platforms. They can use SKMS to align methodologies, harmonize indicators, and to enhance the quality of statistical data metadata.
76 -* **International Organisations** (e.g. [[ILO>>https://www.ilo.org/||target="_blank"]], FAO, [[Eurostat>>https://ec.europa.eu/eurostat||target="_blank"]], [[UNECE>>https://unece.org/ru/homepage||target="_blank"]]): contribute international classifications, standards, and glossaries, and can use SKMS to support semantic interoperability across countries. They benefit from improved alignment of national data and from the ability to publish reference models in a reusable semantic format.
76 +* **International Organisations** (e.g. [[ILO>>https://www.ilo.org/||rel="noopener noreferrer" target="_blank"]], FAO, [[Eurostat>>https://ec.europa.eu/eurostat||rel="noopener noreferrer" target="_blank"]], [[UNECE>>https://unece.org/ru/homepage||rel="noopener noreferrer" target="_blank"]]): contribute international classifications, standards, and glossaries, and can use SKMS to support semantic interoperability across countries. They benefit from improved alignment of national data and from the ability to publish reference models in a reusable semantic format.
77 77  * **Statistical Methodology Experts**: play a key role in reviewing and formalizing statistical definitions, ensuring conceptual clarity and consistency across indicators and classifications. Their contributions strengthen the semantic backbone of statistical domains.
78 78  * **Metadata and Knowledge Managers**: are responsible for curating glossaries, maintaining multilingual terminologies, and ensuring the semantic quality of published content. They use SKMS to build, manage, and share semantic models.
79 79  * **Data Integration and Interoperability Teams**: apply SKMS tools and semantic assets to link data across sources, map between standards, and ensure that contextual meaning is preserved in statistical exchanges. They help implement [[FAIR>>https://www.go-fair.org/]] principles.