1. Classifications and Linked Open Data Formalizing the structure and content of statistical classifications
Item 9.1 Standards Working Group Luxembourg, June 2019 meeting
Danny DELCAMBRE
Eurostat, Unit B5

2. Linked Open Data
Linked Open Data principles according to Tim Berners-Lee
Use URIs to identify things.
Use HTTP URIs so that these things can be looked up (interpreted, "dereferenced").
Provide useful information about what a name identifies when it's looked up, using open standards such as RDF, SPARQL, etc.
Refer to other things using their HTTP URI-based names when publishing data on the Web.

3. How can Linked Open Data benefit from the Official Statistics community
The Linked Open Data community can benefit as a whole from the involvement of the official statistics community:
Availability of high-quality, curated URIs representing different classifications. They can be used as a linking hub for other Linked Open data sources
Development of ontologies that can be used beyond official statistics for the description of data and metadata assets

4. How can Official statistics benefit from Linked Open data
Eurostat
NSI – 1
NSI – …
European linked statistical data portal
Other Datasets (Governments, police, health agencies, etc.)
Visualisation App
Enhance the amount of links between different statistical datasets (e.g. between classifications or between different national datasets) and offer combined views to the users

5. Classifications

6. LOD and semantics
To be able to semantically link objects, these must be described precisely In LOD use is made of vocabularies such as foaf, skos, dp, prov which provide specifications for describing a specific « universe » (e.g. provenance, knowledge organization systems for thesauri, subject lists and taxonomies) These vocabularies can be combined as shown below :

7. For classifications a draft vocabulary called xkos specializes the generic skos vocabulary which is not detailed enough to represent statistical classifications (xkos = skos extension for representing statistical classifications)
Below is a schematic representation of explanatory notes using a combination of skos and xkos vocabularies

8. Currently xkos is a draft specification !
The xkos specification was developed based on the knowledge of the persons who drafted it and following a public consultation The current xkos specification can be found here: Most people involved in the project were not classification experts, which means that it would be useful to investigate whether the specification actually covers all aspects of classifications. That’s what the document that was circulated before the meeting is all about Its purpose is basically to make an inventory of all relevant properties of statistical classifications and correspondence tables with a view to :
harmonize the terminology among us
make sure that all aspects of classifications can be represented using specific attributes (general attributes can always be used but the semantic exploitation of these attributes is limited)

9. What do we expect from you ?
You are invited to
review the document and tell us whether nationally you use additional attributes for describing classifications (please note that we are concentrating here on standard classifications such as ISIC, NACE, COICOP and not on survey classifications which of course have additional attributes such as unit of measure)
The result of this exercise will be used to
draft a general document trying to harmonize the terminology used when talking about statistical classifications
provide input to the xkos people in order to improve their specification

10. Thank you for your attention