Skip to Content

Changes for page About

From version 16.1
edited by Helena
on 2025/06/26 14:48
Change comment: There is no comment for this version
To version 16.14
edited by Helena
on 2025/06/26 15:37
Change comment: There is no comment for this version

Summary

Details

Page properties
Content
... ... @@ -2,11 +2,11 @@
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 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), in accordance with FAIR 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  
... ... @@ -18,21 +18,21 @@
18 18  
19 19  == Linked Data ==
20 20  
21 -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.
21 +The World Wide Web Consortium ([[W3C>>https://www.w3.org/||rel="noopener noreferrer" target="_blank"]]) 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>>https://www.w3.org/standards/||rel="noopener noreferrer" target="_blank"]] — 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.
22 22  
23 -Linked Open Statistical Data (LOSD) refers to statistical datasets published as Linked Data under an open license such as CC BY 4.0, promoting free reuse and wide dissemination. Interoperability is achieved by creating, exchanging, and using LOSD in ways that preserve the meaning and context of the data, regardless of the systems involved.
23 +Linked Open Statistical Data (LOSD) refers to statistical datasets published as Linked Data under an open license such as [[CC BY 4.0>>https://creativecommons.org/licenses/by/4.0/||rel="noopener noreferrer" target="_blank"]], promoting free reuse and wide dissemination. Interoperability is achieved by creating, exchanging, and using LOSD in ways that preserve the meaning and context of the data, regardless of the systems involved.
24 24  
25 25  Human understanding and machine interpreting of statistical data is often difficult due to the lack of formalised domain knowledge and the absence of machine-readable, semantically enriched data. Poor semantic structure means that even published linked data can be hard to discover and accurately relate to domain concepts.
26 26  
27 -The High-Level Group for the Modernisation of Official Statistics (HLG-MOS), under the United Nations Economic Commission for Europe (UNECE), 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), 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.
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 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 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>>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  
... ... @@ -56,7 +56,7 @@
56 56  
57 57  * Enhance the quality of statistical data and metadata
58 58  * Harmonize statistical terminology and classification
59 -* Align with FAIR 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  
... ... @@ -76,23 +76,23 @@
76 76  * **International Organisations** (e.g. ILO, FAO, Eurostat, UNECE): 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 -* **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 principles.
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.
80 80  
81 81  Each of these user groups contributes to the ecosystem of Linked Open Statistical Data, enabling a sustainable and collaborative infrastructure for semantically enhanced statistics.
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 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 88  [[url:https://xmlns.com/foaf/spec/||rel="noopener noreferrer" target="_blank"]]
89 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 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.
91 +* **OWL (Web Ontology Language)** – 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"]].
92 92  [[https:~~/~~/www.w3.org/OWL/>>url:https://www.w3.org/OWL/||rel="noopener noreferrer" target="_blank"]]
93 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 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.
95 +* **RDF 1.1 Concepts and Abstract Syntax** – 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.
96 96  [[https:~~/~~/www.w3.org/TR/rdf11-concepts/>>url:https://www.w3.org/TR/rdf11-concepts/||rel="noopener noreferrer" target="_blank"]]
97 97  * **RDFS (RDF Schema 1.1)** – a vocabulary extension to RDF, providing classes and properties for defining basic ontologies and structuring RDF resources.
98 98  [[https:~~/~~/www.w3.org/TR/rdf-schema/>>url:https://www.w3.org/TR/rdf-schema/||rel="noopener noreferrer" target="_blank"]]