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* Provide semantic assets for reuse to enhance the quality of linked data and metadata, improve comparability, and facilitate cross-domain integration. |
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* 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. |
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+{{box cssClass="box_green"}} |
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== SDMX Implementation == |
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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. |
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== Linked Data == |
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The World Wide Web Consortium (W3C) recommends Linked Data as the most effective way to publish data on the Internet. Linked Data is developed according to the principles of the Semantic Web — a global semantic infrastructure and a set of fundamental rules for representing data on the Internet in a way that allows information systems to interpret its meaning correctly. |
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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. |
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-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. |
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+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. |
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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 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. |
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Combining international experience and our own research, SKMS provides a semantically rich interpretation environment for statistical institutions to: |
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-• Enhance the quality of statistical data and metadata |
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+* Enhance the quality of statistical data and metadata |
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+* Harmonize statistical terminology and classification |
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+* Align with FAIR principles |
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+* Ensure semantic interoperability and reuse |
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+* Facilitate accurate (meta)data interpretation |
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-• Harmonize statistical terminology and classification |
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-• Align with FAIR principles |
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-• Ensure semantic interoperability and reuse |
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The use of SKMS brings the following benefits: |
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-• Adoption of semantic modelling in statistical practice |
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+* Adoption of semantic modelling in statistical practice |
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+* Generation of semantically rich metadata and LOSD sets |
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+* Validation of results using visualization tools |
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-• Generation of semantically rich metadata and LOSD sets |
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-• Validation of results using visualization tools |
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== Key Users == |
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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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The Semantic Knowledge Management System relies on a set of well-established Semantic Web standards and vocabularies: |
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* **FOAF (Friend Of A Friend)** – a vocabulary of named properties and classes for describing people and their relationships, built using RDF and OWL. |
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* **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). |
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+[[https:~~/~~/www.w3.org/TR/vcard-rdf/>>url:https://www.w3.org/TR/vcard-rdf/||rel="noopener noreferrer" target="_blank"]] |
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* **OWL (Web Ontology Language)** – a language for defining and linking ontologies, supporting formal descriptions of concepts, properties, and relationships in the Semantic Web. |
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-[[https:~~/~~/www.w3.org/OWL/>>url:https://www.w3.org/OWL/]] |
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+[[https:~~/~~/www.w3.org/OWL/>>url:https://www.w3.org/OWL/||rel="noopener noreferrer" target="_blank"]] |
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* **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. |
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+[[https:~~/~~/www.dublincore.org/specifications/dublin-core/dces/>>https://https:www.dublincore.orgspecificationsdublin-coredces||rel="noopener noreferrer" target="_blank"]] |
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* **RDF 1.1 Concepts and Abstract Syntax** – the foundational knowledge representation model of the Semantic Web, defining how RDF data is structured using triples. |
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* **RDFS (RDF Schema 1.1)** – a vocabulary extension to RDF, providing classes and properties for defining basic ontologies and structuring RDF resources. |
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-[[https:~~/~~/www.w3.org/TR/rdf-schema/>>url:https://www.w3.org/TR/rdf-schema/]] |
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+[[https:~~/~~/www.w3.org/TR/rdf-schema/>>url:https://www.w3.org/TR/rdf-schema/||rel="noopener noreferrer" target="_blank"]] |
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* **RDF Data Cube Vocabulary** – a W3C vocabulary for publishing multidimensional statistical data in RDF, compatible with the SDMX cube model. |
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+[[https:~~/~~/www.w3.org/TR/vocab-data-cube/>>url:https://www.w3.org/TR/vocab-data-cube/||rel="noopener noreferrer" target="_blank"]] |
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* **SDMX (Statistical Data and Metadata Exchange)** – an international standard for the exchange of statistical data and metadata, supported by key statistical organizations. |
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-[[https:~~/~~/sdmx.org/>>url:https://sdmx.org/]] |
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+[[https:~~/~~/sdmx.org/>>url:https://sdmx.org/||rel="noopener noreferrer" target="_blank"]] |
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* **SKOS (Simple Knowledge Organization System)** – a W3C standard for representing knowledge organization systems such as thesauri, taxonomies, and classifications. |
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* **SKOS-XL (SKOS eXtension for Labels)** – an extension of SKOS that allows for richer descriptions and relationships between lexical labels. |
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-[[https:~~/~~/www.w3.org/TR/skos-reference/skos-xl.html>>url:https://www.w3.org/TR/skos-reference/skos-xl.html]] |
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+[[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"]] |
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* **XKOS (SKOS extension for statistical classifications)** – a vocabulary extending SKOS for describing statistical classifications and code lists, jointly developed by INSEE and Eurostat. |
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-https:~/~/rdf-vocabulary.ddialliance.org/xkos.html |
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+[[https:~~/~~/rdf-vocabulary.ddialliance.org/xkos.html>>url:https://rdf-vocabulary.ddialliance.org/xkos.html||rel="noopener noreferrer" target="_blank"]] |
