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30 30  
31 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.
32 32  
33 -A key enabler of FAIR 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 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 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.
36 36  
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54 54  
55 55  Combining international experience and our own research, SKMS provides a semantically rich interpretation environment for statistical institutions to:
56 56  
57 -•             Enhance the quality of statistical data and metadata
57 +* Enhance the quality of statistical data and metadata
58 +* Harmonize statistical terminology and classification
59 +* Align with FAIR principles
60 +* Ensure semantic interoperability and reuse
61 +* Facilitate accurate (meta)data interpretation
58 58  
59 -•             Harmonize statistical terminology and classification
60 -
61 -•             Align with FAIR principles
62 -
63 -•             Ensure semantic interoperability and reuse
64 -
65 -•             Facilitate accurate (meta)data interpretation
66 -
67 67  The use of SKMS brings the following benefits:
68 68  
69 -•             Adoption of semantic modelling in statistical practice
65 +* Adoption of semantic modelling in statistical practice
66 +* Generation of semantically rich metadata and LOSD sets
67 +* Validation of results using visualization tools
70 70  
71 -•             Generation of semantically rich metadata and LOSD sets
72 -
73 -•             Validation of results using visualization tools
74 -
75 75  == Key Users ==
76 76  
77 77  SKMS is designed to support a wide range of stakeholders engaged in the creation, management, dissemination, and use of statistical knowledge as well as the development of linked statistical data.
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91 91  The Semantic Knowledge Management System relies on a set of well-established Semantic Web standards and vocabularies:
92 92  
93 93  * **FOAF (Friend Of A Friend)** – a vocabulary of named properties and classes for describing people and their relationships, built using RDF and OWL.
94 -https:~/~/xmlns.com/foaf/spec/
88 +[[url:https://xmlns.com/foaf/spec/||rel="noopener noreferrer" target="_blank"]]
95 95  * **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).
96 -[[https:~~/~~/www.w3.org/TR/vcard-rdf/>>url:https://www.w3.org/TR/vcard-rdf/]]
90 +[[https:~~/~~/www.w3.org/TR/vcard-rdf/>>url:https://www.w3.org/TR/vcard-rdf/||rel="noopener noreferrer" target="_blank"]]
97 97  * **OWL (Web Ontology Language)** – a language for defining and linking ontologies, supporting formal descriptions of concepts, properties, and relationships in the Semantic Web.
98 -[[https:~~/~~/www.w3.org/OWL/>>url:https://www.w3.org/OWL/]]
92 +[[https:~~/~~/www.w3.org/OWL/>>url:https://www.w3.org/OWL/||rel="noopener noreferrer" target="_blank"]]
99 99  * **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.
100 -https:~/~/www.dublincore.org/specifications/dublin-core/dces/
94 +[[https:~~/~~/www.dublincore.org/specifications/dublin-core/dces/>>https://https:www.dublincore.orgspecificationsdublin-coredces||rel="noopener noreferrer" target="_blank"]]
101 101  * **RDF 1.1 Concepts and Abstract Syntax** – the foundational knowledge representation model of the Semantic Web, defining how RDF data is structured using triples.
102 -[[https:~~/~~/www.w3.org/TR/rdf11-concepts/>>url:https://www.w3.org/TR/rdf11-concepts/]]
96 +[[https:~~/~~/www.w3.org/TR/rdf11-concepts/>>url:https://www.w3.org/TR/rdf11-concepts/||rel="noopener noreferrer" target="_blank"]]
103 103  * **RDFS (RDF Schema 1.1)** – a vocabulary extension to RDF, providing classes and properties for defining basic ontologies and structuring RDF resources.
104 -[[https:~~/~~/www.w3.org/TR/rdf-schema/>>url:https://www.w3.org/TR/rdf-schema/]]
98 +[[https:~~/~~/www.w3.org/TR/rdf-schema/>>url:https://www.w3.org/TR/rdf-schema/||rel="noopener noreferrer" target="_blank"]]
105 105  * **RDF Data Cube Vocabulary** – a W3C vocabulary for publishing multidimensional statistical data in RDF, compatible with the SDMX cube model.
106 -[[https:~~/~~/www.w3.org/TR/vocab-data-cube/>>url:https://www.w3.org/TR/vocab-data-cube/]]
100 +[[https:~~/~~/www.w3.org/TR/vocab-data-cube/>>url:https://www.w3.org/TR/vocab-data-cube/||rel="noopener noreferrer" target="_blank"]]
107 107  * **SDMX (Statistical Data and Metadata Exchange)** – an international standard for the exchange of statistical data and metadata, supported by key statistical organizations.
108 -[[https:~~/~~/sdmx.org/>>url:https://sdmx.org/]]
102 +[[https:~~/~~/sdmx.org/>>url:https://sdmx.org/||rel="noopener noreferrer" target="_blank"]]
109 109  * **SKOS (Simple Knowledge Organization System)** – a W3C standard for representing knowledge organization systems such as thesauri, taxonomies, and classifications.
110 -[[https:~~/~~/www.w3.org/TR/skos-reference/>>url:https://www.w3.org/TR/skos-reference/]]
104 +[[https:~~/~~/www.w3.org/TR/skos-reference/>>url:https://www.w3.org/TR/skos-reference/||rel="noopener noreferrer" target="_blank"]]
111 111  * **SKOS-XL (SKOS eXtension for Labels)** – an extension of SKOS that allows for richer descriptions and relationships between lexical labels.
112 -[[https:~~/~~/www.w3.org/TR/skos-reference/skos-xl.html>>url:https://www.w3.org/TR/skos-reference/skos-xl.html]]
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"]]
113 113  * **XKOS (SKOS extension for statistical classifications)** – a vocabulary extending SKOS for describing statistical classifications and code lists, jointly developed by INSEE and Eurostat.
114 -https:~/~/rdf-vocabulary.ddialliance.org/xkos.html
108 +[[https:~~/~~/rdf-vocabulary.ddialliance.org/xkos.html>>url:https://rdf-vocabulary.ddialliance.org/xkos.html||rel="noopener noreferrer" target="_blank"]]
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