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Summary

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2 2  
3 3  The overall goal of implementing SKMS in statistical organizations is to increase the effectiveness and potential of using statistical data by ensuring unambiguous and semantically rich interpretation — both by people and information systems.
4 4  
5 -Moving towards [[FAIR>>https://www.go-fair.org/||target="_blank"]] statistics and interoperable statistical data SKMS focuses on the following objectives:
5 +Moving towards [[FAIR>>https://www.go-fair.org/||rel="noopener noreferrer" target="_blank"]] statistics and interoperable statistical data SKMS focuses on the following objectives:
6 6  
7 7  * Provide a shared semantic environment that brings together documents, glossaries, classifications, and standards into a unified, machine-readable framework (factory) for the creation, dissemination and interpretation of Linked Open Statistical Data (LOSD).
8 8  * Create an extensible, interconnected context for data modelling based on semantic assets via both machine-readable forms interpretable by information systems, and visual representations understandable by people.
9 -* Enable the preparation and dissemination of LOSD and semantically rich metadata ([[“smart” metadata>>http://cosmos-conference.org/index.html||rel="noopener noreferrer" target="_blank"]]), in accordance with [[FAIR>>https://www.go-fair.org/||target="_blank"]] principles, ensuring semantic interoperability.
9 +* Enable the preparation and dissemination of LOSD and semantically rich metadata ([[“smart” metadata>>http://cosmos-conference.org/index.html||rel="noopener noreferrer" target="_blank"]]), in accordance with [[FAIR>>https://www.go-fair.org/||rel="noopener noreferrer" target="_blank"]] principles, ensuring semantic interoperability.
10 10  * Provide semantic assets for reuse to enhance the quality of linked data and metadata, improve comparability, and facilitate cross-domain integration.
11 11  * Foster collaboration between IT professionals and statistical experts to co-develop semantic models, aligning terminology and classifications, preparing informative indicators and LOSD sets descriptions to ensure their relevance, usability, and operational value.
12 12  
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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) 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.
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.
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”) metadata — metadata that is standardised (understandable and reusable across contexts), active (capable of driving statistical processes), and aligned with the [[FAIR>>https://www.go-fair.org/||target="_blank"]] principles: Findable, Accessible, Interoperable, and Reusable.
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.
30 30  
31 -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.
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  
33 -A key enabler of [[FAIR>>https://www.go-fair.org/||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.
33 +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.
34 34  
35 -The adoption of LOSD creates new opportunities for discovering, searching, comparing, and integrating statistical data from multiple sources through Semantic Web technologies, including semantic integration methods. This approach enables the achievement of the highest level of data maturity according to the 5-star model proposed by Tim Berners-Lee.
35 +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 proposed by Tim Berners-Lee.
36 36  
37 37  == Operational Cycle ==
38 38  
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56 56  
57 57  * Enhance the quality of statistical data and metadata
58 58  * Harmonize statistical terminology and classification
59 -* Align with [[FAIR>>https://www.go-fair.org/||target="_blank"]] principles
59 +* Align with [[FAIR>>https://www.go-fair.org/||rel="noopener noreferrer" target="_blank"]] principles
60 60  * Ensure semantic interoperability and reuse
61 61  * Facilitate accurate (meta)data interpretation
62 62  
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82 82  
83 83  == Standards and Technologies Used ==
84 84  
85 -The Semantic Knowledge Management System relies on a set of well-established Semantic Web standards and vocabularies:
85 +The Semantic Knowledge Management System relies on a set of well-established [[Semantic Web>>https://www.w3.org/standards/||rel="noopener noreferrer" target="_blank"]] standards and vocabularies:
86 86  
87 -* **FOAF (Friend Of A Friend)** – a vocabulary of named properties and classes for describing people and their relationships, built using RDF and OWL.
88 -[[url:https://xmlns.com/foaf/spec/||rel="noopener noreferrer" target="_blank"]]
89 -* **vCard (The Electronic Business Card)** – a data format for representing and exchanging contact information about individuals and organizations (e.g. for phonebooks or email clients).
90 -[[https:~~/~~/www.w3.org/TR/vcard-rdf/>>url:https://www.w3.org/TR/vcard-rdf/||rel="noopener noreferrer" target="_blank"]]
91 -* **OWL (Web Ontology Language)** – a language for defining and linking ontologies, supporting formal descriptions of concepts, properties, and relationships in the Semantic Web.
92 -[[https:~~/~~/www.w3.org/OWL/>>url:https://www.w3.org/OWL/||rel="noopener noreferrer" target="_blank"]]
93 -* **Dublin Core™ Metadata Initiative (DCMI)** – a standard set of metadata terms used to describe a wide range of resources, including elements, encoding schemes, and syntax guidelines.
94 -[[https:~~/~~/www.dublincore.org/specifications/dublin-core/dces/>>https://https:www.dublincore.orgspecificationsdublin-coredces||rel="noopener noreferrer" target="_blank"]]
95 -* **RDF 1.1 Concepts and Abstract Syntax** – the foundational knowledge representation model of the Semantic Web, defining how RDF data is structured using triples.
96 -[[https:~~/~~/www.w3.org/TR/rdf11-concepts/>>url:https://www.w3.org/TR/rdf11-concepts/||rel="noopener noreferrer" target="_blank"]]
97 -* **RDFS (RDF Schema 1.1)** – a vocabulary extension to RDF, providing classes and properties for defining basic ontologies and structuring RDF resources.
98 -[[https:~~/~~/www.w3.org/TR/rdf-schema/>>url:https://www.w3.org/TR/rdf-schema/||rel="noopener noreferrer" target="_blank"]]
99 -* **RDF Data Cube Vocabulary** – a W3C vocabulary for publishing multidimensional statistical data in RDF, compatible with the SDMX cube model.
100 -[[https:~~/~~/www.w3.org/TR/vocab-data-cube/>>url:https://www.w3.org/TR/vocab-data-cube/||rel="noopener noreferrer" target="_blank"]]
101 -* **SDMX (Statistical Data and Metadata Exchange)** – an international standard for the exchange of statistical data and metadata, supported by key statistical organizations.
102 -[[https:~~/~~/sdmx.org/>>url:https://sdmx.org/||rel="noopener noreferrer" target="_blank"]]
103 -* **SKOS (Simple Knowledge Organization System)** – a W3C standard for representing knowledge organization systems such as thesauri, taxonomies, and classifications.
104 -[[https:~~/~~/www.w3.org/TR/skos-reference/>>url:https://www.w3.org/TR/skos-reference/||rel="noopener noreferrer" target="_blank"]]
105 -* **SKOS-XL (SKOS eXtension for Labels)** – an extension of SKOS that allows for richer descriptions and relationships between lexical labels.
106 -[[https:~~/~~/www.w3.org/TR/skos-reference/skos-xl.html>>url:https://www.w3.org/TR/skos-reference/skos-xl.html||rel="noopener noreferrer" target="_blank"]]
107 -* **XKOS (SKOS extension for statistical classifications)** – a vocabulary extending SKOS for describing statistical classifications and code lists, jointly developed by INSEE and Eurostat.
108 -[[https:~~/~~/rdf-vocabulary.ddialliance.org/xkos.html>>url:https://rdf-vocabulary.ddialliance.org/xkos.html||rel="noopener noreferrer" target="_blank"]]
87 +* [[**FOAF (Friend Of A Friend)**>>https://xmlns.com/foaf/spec/||rel="noopener noreferrer" target="_blank"]] – a vocabulary of named properties and classes for describing people and their relationships, built using RDF and OWL.
88 +* [[**vCard (The Electronic Business Card)**>>https://www.w3.org/TR/vcard-rdf/||rel="noopener noreferrer" target="_blank"]] – a data format for representing and exchanging contact information about individuals and organizations (e.g. for phonebooks or email clients).
89 +* [[**OWL (Web Ontology Language)**>>https://www.w3.org/OWL/||rel="noopener noreferrer" target="_blank"]] – a language for defining and linking ontologies, supporting formal descriptions of concepts, properties, and relationships in the [[Semantic Web>>https://www.w3.org/standards/||rel="noopener noreferrer" target="_blank"]].
90 +* [[**Dublin Core™ Metadata Initiative (DCMI)**>>https://www.dublincore.org/specifications/dublin-core/dces/||rel="noopener noreferrer" target="_blank"]] – a standard set of metadata terms used to describe a wide range of resources, including elements, encoding schemes, and syntax guidelines.
91 +* [[**RDF 1.1 Concepts and Abstract Syntax**>>https://www.w3.org/TR/rdf11-concepts/||rel="noopener noreferrer" target="_blank"]] – the foundational knowledge representation model of the [[Semantic Web>>https://www.w3.org/standards/||rel="noopener noreferrer" target="_blank"]], defining how RDF data is structured using triples.
92 +* [[**RDFS (RDF Schema 1.1)**>>https://www.w3.org/TR/rdf-schema/||rel="noopener noreferrer" target="_blank"]] – a vocabulary extension to RDF, providing classes and properties for defining basic ontologies and structuring RDF resources.
93 +* [[**RDF Data Cube Vocabulary**>>https://www.w3.org/TR/vocab-data-cube/||rel="noopener noreferrer" target="_blank"]] – a W3C vocabulary for publishing multidimensional statistical data in RDF, compatible with the SDMX cube model.
94 +* [[**SDMX (Statistical Data and Metadata Exchange)**>>https://sdmx.org/||rel="noopener noreferrer" target="_blank"]] – an international standard for the exchange of statistical data and metadata, supported by key statistical organizations.
95 +* [[**SKOS (Simple Knowledge Organization System)**>>https://www.w3.org/TR/skos-reference/||rel="noopener noreferrer" target="_blank"]] – a W3C standard for representing knowledge organization systems such as thesauri, taxonomies, and classifications.
96 +* [[**SKOS-XL (SKOS eXtension for Labels)**>>https://www.w3.org/TR/skos-reference/skos-xl.html||rel="noopener noreferrer" target="_blank"]] – an extension of SKOS that allows for richer descriptions and relationships between lexical labels.
97 +* [[**XKOS (SKOS extension for statistical classifications)**>>https://rdf-vocabulary.ddialliance.org/xkos.html||rel="noopener noreferrer" target="_blank"]] – a vocabulary extending SKOS for describing statistical classifications and code lists, jointly developed by INSEE and Eurostat.