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edited by Artur
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edited by Helena
on 2025/06/26 14:46

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--(% class="wikigeneratedid" id="HTheproject22InteroperabilityBasis22isanopen2Cnon-profitinitiativeaimedatovercomingtechnologicalandorganizationalbarriersthathindertheeffectiveexchangeanddisseminationofLinkedData.Itsmaingoalistointegrateexistingdataexchangestandards2Cclassifications2CandreferencesystemsintotheSemanticWebenvironmenttoachievesustainablesemanticinteroperabilityacrossawiderangeofusersandusecases." %)
--The project "//**Interoperability Basis**//" is an open, non-profit initiative aimed at overcoming technological and organizational barriers that hinder the effective exchange and dissemination of Linked Data. Its main goal is to integrate existing data exchange standards, classifications, and reference systems into the [[Semantic Web>>https://www.w3.org/2001/sw/wiki/Main_Page||rel="noopener noreferrer" target="_blank"]] environment to achieve sustainable semantic interoperability across a wide range of users and use cases.
++== Goals and Objectives ==
--== Relevance and Challenges ==
++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.
--Semantic Web technologies are among the key enablers of digital transformation. Over the past 15 years, the [[Linked Data (LD)>>https://www.w3.org/DesignIssues/LinkedData||rel="noopener noreferrer" target="_blank"]] approach has become a globally recognized practice for publishing and interlinking structured data on the web. Today, thousands of data providers worldwide follow these principles, publishing open datasets, metadata services via [[SPARQL>>url:https://www.w3.org/TR/sparql11-overview/||rel="noopener noreferrer" target="_blank"]] endpoints (interfaces for querying data using SPARQL), ontologies, and knowledge graphs.
++Moving towards FAIR statistics and interoperable statistical data SKMS focuses on the following objectives:
--Despite this progress, the number of LD datasets in critical domains such as economics, statistics, and finance remain limited. Our research reveals several systemic barriers:
++* 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).
++* 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.
++* Enable the preparation and dissemination of LOSD and semantically rich metadata (“smart” metadata), in accordance with FAIR principles, ensuring semantic interoperability.
++* Provide semantic assets for reuse to enhance the quality of linked data and metadata, improve comparability, and facilitate cross-domain integration.
++* 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.
--* **Paradigm mismatch**: Modern data exchange standards (such as [[SDMX>>url:https://sdmx.org/||rel="noopener noreferrer" target="_blank"]], [[SIMS (ESS, ESMS, ESQRS)>>url:https://ec.europa.eu/eurostat/documents/64157/4373903/SIMS-2-0-Revised-standards-November-2015-ESSC-final.pdf/47c0b80d-0e19-4777-8f9e-28f89f82ce18||rel="noopener noreferrer" target="_blank"]], [[DDI>>https://ddialliance.org/||rel="noopener noreferrer" target="_blank"]], [[XBRL>>url:https://www.xbrl.org/Specification/XBRL-2.1/REC-2003-12-31/XBRL-2.1-REC-2003-12-31+corrected-errata-2013-02-20.html||rel="noopener noreferrer" target="_blank"]], [[UN/EDIFACT>>url:https://unece.org/trade/uncefact||rel="noopener noreferrer" target="_blank"]], [[NIEM>>url:https://www.niem.gov/||rel="noopener noreferrer" target="_blank"]], [[HL7>>url:https://www.hl7.org/||rel="noopener noreferrer" target="_blank"]]) are developed within an object-oriented paradigm, which is not inherently compatible with Semantic Web principles.
--* **Lack of identifiers**: Widely used classifications and reference systems are typically published as tables, codelists, and hierarchies without persistent identifiers ([[URIs>>https://www.w3.org/Addressing/URL/uri-spec.html||rel="noopener noreferrer" target="_blank"]]), making integration in a Linked Data environment difficult.
--* **URI persistence issues**: There is limited support for persistent URIs for elements of standards, classifications, and references, as well as poor handling of URI dereferencing, which reduces the long-term usability of data.
++{{box cssClass="box_green"}}
++== SDMX Implementation ==
--**//Interoperability Basis//** aims to address these issues and support a broad transition to semantic technologies, enabling Linked Data to become a universal medium for data exchange and dissemination, in line with the [[FAIR principles>>url:https://www.go-fair.org/fair-principles/||rel="noopener noreferrer" target="_blank"]].
++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.
++{{/box}}
--== Goals and Objectives ==
++== Linked Data ==
--The goal of the //**Interoperability Basis**// is to overcome systemic barriers to the widespread adoption of Linked Data by semantically enabling existing object-oriented standards, classifications, and reference frameworks.
++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.
--We have created an open, non-profit platform that:
++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.
--* Enables the transformation of existing data exchange standards into semantic formats and loads them into a semantic knowledge management system.
--* Supports a controlled namespace environment.
--* Builds robust infrastructure for persistent URI management and dereferencing.
--* Provides semantic models for constructing Linked Data and semantically annotated web services.
++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.
--We envision a future in which organizations, researchers, and developers interact with data without technological or semantic barriers. Semantic enrichment and contextual description within knowledge management systems ensure precise and unambiguous interpretation of information across diverse domains.
++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.
--//**Interoperability Basis**// is not just a tool, but a comprehensive platform for the collaborative creation, evolution, and sharing of knowledge, in which every participant becomes part of a global semantic network of interoperable data, standards, and tools.
++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.
--We transform interoperability from an abstract promise into a tangible and achievable reality.
++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.
--== Collaboration ==
++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.
--We invite partners to join us in the collaborative development of the //**Interoperability Basis**// international initiative:
++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.
--* **International organizations**: institutions developing and maintaining data standards (e.g., [[SDMX>>url:https://sdmx.org/||rel="noopener noreferrer" target="_blank"]], [[SIMS>>url:https://ec.europa.eu/eurostat/documents/64157/4373903/SIMS-2-0-Revised-standards-November-2015-ESSC-final.pdf/47c0b80d-0e19-4777-8f9e-28f89f82ce18||rel="noopener noreferrer" target="_blank"]], [[XBRL>>url:https://www.xbrl.org/||rel="noopener noreferrer" target="_blank"]], [[DDI>>url:https://ddialliance.org/||rel="noopener noreferrer" target="_blank"]], [[NIEM>>url:https://www.niem.gov/||rel="noopener noreferrer" target="_blank"]])
--** (((
--Can benefit from using a sustainable platform for expressing their standards as semantic assets: transform, enrich and align their structures and semantics; apply tools to convert/develop data schemas, vocabularies, and code lists into RDF/OWL; assign persistent URIs; manage the sections of their standards in the common namespace; maintain these discoverable, reusable, and interoperable assets across domains.
--)))
--* **Governments and public administrations**: state institutions seeking to improve data transparency, openness, and interoperability in public sector data and metadata
--** (((
--Can release high-value datasets and classification systems based on semantically transformed, enriched and aligned standards provided by the platform. They can also benefit from sustainable publishing of public data using persistent URIs, semantically enhanced classifications, and interoperable APIs, while also aligning their open data with semantic standards to ensure long-term transparency and cross-border interoperability.
--)))
--* **Enterprises**: organizations focused on building corporate ecosystems based on semantic core to implement innovative semantic applications, web services, and data integration solutions
--** (((
--Can leverage the platform as a foundation for building a corporate semantic core and for managing enterprise metadata in a consistent, standards-based way. This shared semantic infrastructure accelerates information sharing, supports digital transformation, and enables the development of AI applications that rely on high-quality, interoperable data. The platform’s persistent URIs and standardized semantic models help reduce implementation costs for scalable enterprise solutions
--)))
--* **Academic and research institutions**: universities and research centers working on semantic technologies and knowledge management across various domains
--** (((
--Can benefit from an open, authoritative interoperability infrastructure that fosters international collaboration across research teams and disciplines. The platform supports joint pilot projects, semantic modeling studies, and contributions to the development and alignment of data standards, enabling researchers to actively participate in shaping the semantic foundations of global data exchange
--)))
--* **Semantic Web technology developers**: experts creating tools and platforms for working with [[RDF>>url:https://www.w3.org/RDF/||rel="noopener noreferrer" target="_blank"]], [[OWL>>url:https://www.w3.org/OWL/||rel="noopener noreferrer" target="_blank"]], [[SPARQL>>url:https://www.w3.org/TR/sparql11-overview/||rel="noopener noreferrer" target="_blank"]], and other linked data technologies
--** (((
--Can use the platform’s namespace to build and publish their own semantic assets with persistent URIs. Participation enables integration with real-world data ecosystems, early access to new semantic standards, and visibility within a community working on applied Linked Data solutions.
--)))
++== Operational Cycle ==
--== Stakeholders ==
++Effective implementation of LOSD requires an open semantically rich interpretation environment. The set of systems and tools under SKMS “umbrella” forms a unified terminological and methodological basis for the development of rich semantic models and then provides the possibility of their use for the preparation, dissemination and interpretation of linked data and rich metadata. An important principle underlying the proposed methods and tools is to ensure the collaboration of IT specialists and statistical experts.
--//**Interoperability Basis**// significantly increases the effectiveness of organizations committed to publishing Linked Data and smart metadata, promoting sustainable data exchange and integration, and establishing a reliable foundation for interoperability.
--The project is designed for:
++The full operational cycle consists of seven stages:
--* **Organizations publishing open data**
--* **Statistical and analytical agencies**
--* **Governmental and international bodies**
--* **Academic and research groups**
--* **IT companies and startups**
++1. Collection and systematization of methodological documents (creation of an electronic library), adding annotations, discovering terms-candidates and primary markup with related terms and documents. Publishing documents in original structured form with hypertext markup in a specialized "Methodology" section.
++1. The development of glossaries (the formation of detailed terminological articles), indicators descriptions based on the analysis of methodological documents, and then the generation of corresponding semantic assets. Refinement of hyper-text markup in accordance with modelled glossaries.
++1. Publishing semantic assets generated in the SKMS.
++1. Development, aligning and cataloging of necessary SA, code lists or other models of statistical domains in accordance with semantic standards.
++1. Importing datasets from external sources or data warehouses (DWH). Transformation of datasets using the RDF Data Cube Vocabulary, semantic enrichment.
++1. Visualization and validation of semantic models and LOSD sets.
++1. Construction of rich metadata that is transmitted for publishing in external analytical systems.
--By using //**Interoperability Basis**//, organizations not only apply advanced approaches in their work but also help shape an environment where high-quality data and open standards drive digital transformation and innovation.
++SKMS is based on the XWiki extension to using semantic technologies. It provides special templates for publishing documents, glossary terms, and indicator descriptions. They are used by domain experts to formalize statistical knowledge and provide their human-readable representation fixed in SAs. The LOSD pipeline is supported by generators and constructors developed to automate the formation of LOSD, semantic models and semantically enriched metadata. SKMS may be integrated with a cataloging service that supports not only the organisation of semantic assets, but also their visualization, access, and dissemination through standard interfaces such as OpenAPI and SPARQL Endpoints.
--== Future Development ==
++== Benefits ==
--//**Interoperability Basis**// is a growing initiative committed to enabling true interoperability, semantic integration of data, and accelerating the transition to Linked Data. The project will continue to expand the use of semantic technologies across domains such as statistics, finance, healthcare, international trade, and more.
++Combining international experience and our own research, SKMS provides a semantically rich interpretation environment for statistical institutions to:
--To support AI-driven applications, effective use of [[LLMs>>https://arxiv.org/html/2402.06196v2||rel="noopener noreferrer" target="_blank"]]: requires access to high-quality, structured, and semantically enriched data. //**Interoperability Basis**// enables:
++* Enhance the quality of statistical data and metadata
++* Harmonize statistical terminology and classification
++* Align with FAIR principles
++* Ensure semantic interoperability and reuse
++* Facilitate accurate (meta)data interpretation
--* **Access to formalized knowledge and classifications** — through RDF and OWL, AI models receive well-defined concepts with clear contextual relationships.
--* **Machine-processable structures** — semantic models and annotated APIs allow LLMs to interpret data without manual reformatting.
--* **Enrichment of training datasets** — integrating semantically described thesauri, glossaries, standards, and statistical datasets into model training pipelines.
--* **Support for explainable AI** — modern LLMs can leverage ontologies and knowledge graphs not just to generate responses, but to construct logical, interpretable outputs when analyzing Linked Data, referencing verifiable sources and using contextualized semantics.
++The use of SKMS brings the following benefits:
--//**Interoperability Basis**// provides a vital layer of infrastructure for the development of intelligent agents capable of interpreting and applying structured knowledge in both applied and research domains.
++* Adoption of semantic modelling in statistical practice
++* Generation of semantically rich metadata and LOSD sets
++* Validation of results using visualization tools
++
++== Key Users ==
++
++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.
++
++Each user group contributes to and benefits from the semantic foundation provided by SKMS:
++
++* **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.
++* **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.
++* **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.
++* **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.
++* **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.
++
++Each of these user groups contributes to the ecosystem of Linked Open Statistical Data, enabling a sustainable and collaborative infrastructure for semantically enhanced statistics.
++
++== Standards and Technologies Used ==
++
++The Semantic Knowledge Management System relies on a set of well-established Semantic Web standards and vocabularies:
++
++* **FOAF (Friend Of A Friend)** – a vocabulary of named properties and classes for describing people and their relationships, built using RDF and OWL.
++[[url:https://xmlns.com/foaf/spec/]]
++* **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).
++[[https:~~/~~/www.w3.org/TR/vcard-rdf/>>url:https://www.w3.org/TR/vcard-rdf/]]
++* **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/OWL/>>url:https://www.w3.org/OWL/]]
++* **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.
++[[https:~~/~~/www.dublincore.org/specifications/dublin-core/dces/>>https://https:www.dublincore.orgspecificationsdublin-coredces]]
++* **RDF 1.1 Concepts and Abstract Syntax** – the foundational knowledge representation model of the Semantic Web, defining how RDF data is structured using triples.
++[[https:~~/~~/www.w3.org/TR/rdf11-concepts/>>url:https://www.w3.org/TR/rdf11-concepts/]]
++* **RDFS (RDF Schema 1.1)** – a vocabulary extension to RDF, providing classes and properties for defining basic ontologies and structuring RDF resources.
++[[https:~~/~~/www.w3.org/TR/rdf-schema/>>url:https://www.w3.org/TR/rdf-schema/]]
++* **RDF Data Cube Vocabulary** – a W3C vocabulary for publishing multidimensional statistical data in RDF, compatible with the SDMX cube model.
++[[https:~~/~~/www.w3.org/TR/vocab-data-cube/>>url:https://www.w3.org/TR/vocab-data-cube/]]
++* **SDMX (Statistical Data and Metadata Exchange)** – an international standard for the exchange of statistical data and metadata, supported by key statistical organizations.
++[[https:~~/~~/sdmx.org/>>url:https://sdmx.org/]]
++* **SKOS (Simple Knowledge Organization System)** – a W3C standard for representing knowledge organization systems such as thesauri, taxonomies, and classifications.
++[[https:~~/~~/www.w3.org/TR/skos-reference/>>url:https://www.w3.org/TR/skos-reference/]]
++* **SKOS-XL (SKOS eXtension for Labels)** – an extension of SKOS that allows for richer descriptions and relationships between lexical labels.
++[[https:~~/~~/www.w3.org/TR/skos-reference/skos-xl.html>>url:https://www.w3.org/TR/skos-reference/skos-xl.html]]
++* **XKOS (SKOS extension for statistical classifications)** – a vocabulary extending SKOS for describing statistical classifications and code lists, jointly developed by INSEE and Eurostat.
++[[https:~~/~~/rdf-vocabulary.ddialliance.org/xkos.html>>url:https://rdf-vocabulary.ddialliance.org/xkos.html]]

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