Semantic Statistics: Bringing Together SDMX and SCOVO
Richard Cyganiak

Simon Field

Arofan Gregory

Digital Enterprise Research Institute
NUI Galway, Lower Dangan
Galway, Ireland

Office for National Statistics
Cardiff Road
Newport NP10 8XG

metadata technology
5335 North Nina Drive
Tucson, Arizona AZ 85704

richard@cyganiak.desimon.field@ons.gsi.gov.uk

arofan.gregory@metadatatechnology.com

Wolfgang Halb

Jeni Tennison

Institute of Information Systems
JOANNEUM RESEARCH
Graz, Austria

The Stationery Office
Mandela Way
London SE1 5SS

wolfgang.halb@joanneum.atjeni.tennison@tso.co.uk

ABSTRACT
Whether it's population, income, unemployment or interest rates,
statistical data is a fundamental source of information for analysis
and visualisations. Many publishers of statistics use SDMX torepresent statistics and make them available through web services.
The linked data principles of identifying items with HTTP URIs
and representing data using RDF provide some benefits (thoughalso some costs) for statistical publishing. This paper describes
how the SDMX information model can be used with linked data
and RDF and describes some ongoing work to explore the impactof doing so.

Categories and Subject Descriptors
H.4 [Information Systems]: Information Systems Applications

General Terms
Design, Standardization

Keywords
statistics, linked data, RDF, SDMX, SCOVO, open data

1. INTRODUCTION
Statistical data is the life blood of the interesting mash-ups andvisualisations we see on the web. It provides the raw numbers thatdesigners love to turn into graphs and charts. More importantly,
its analysis enables policy makers to make predications, plan andadjust. So much of the data that we have is statistical data. But
how can we bring together the existing standards for transferring
statistical data and the linked data approach [1]? What advantages
does this bring?
The current standard for statistical organisations to produce theirstatistical data is through Statistical Data and Metadata Exchange(SDMX) [2]. This standard covers everything from how torepresent statistical data in flat files and as XML, to thedefinitions of the dimensions and attributes of observations, to
how to discover statistical dataset flows through a central registry.
SDMX is used by organisations such as the U.S. Federal Reserve
Board, the European Central Bank, Eurostat, the WHO, the IMF,
and the World Bank. The Organisation for Economic Cooperation
and Development (OECD) and the UN expect the publishers ofnational statistics, such as the Office for National Statistics (ONS)in the UK, to produce their statistical data using SDMX so thatthese can be aggregated on an international level.
At the same time, the UK Government has made a commitment tomake public data available on the web using linked data standards
to enable its widespread re-use. The government publishes large
volumes of statistical data and the Office for National Statistics
has invested heavily in technology for managing and publishingstatistical data on the web. Individual departments and agencies
are also publishing increasing volumes of statistical data, in
specialist XML-based formats such as LGDx1, in CSV, as well as
in other formats that generally curtail their wider combination andre-use, such as Excel or PDF.
The UK Government has to reconcile the requirement to sharestatistical data with other statistics authorities using SDMX withthe wider requirement to enable re-use of statistical data on the
web using linked data standards. The challenge is how to marrythese different requirements in a pragmatic way for publishers andconsumers alike. Publishers are concerned about publishing data
responsibly and avoiding the accidental mistinterpretation of
statistical data while the majority of consumers care about theease of accessing, querying and processing statistical data.
In practice, this means publishing statistical data using HTTP
URIs for datasets, time series and individual observations. These
enable both publishers and third parties to annotate and reference
statistical data on the web, which helps to build trust with thoseengaging in conversations about the data. Using the RDF data
model enables consumers to query statistical data in standard
ways and to enhance statistical data by mixing it with other linked
data.

Copyright is held by the author/owner(s).
LDOW 2010, April 27, 2010, Raleigh, North Carolina.
1

http://neighbourhood.statistics.gov.uk/dissemination/Info.do?page=nde.htm

Pragmatically, however, we must adopt approaches that utilise
and build on existing data standards and technology investments
rather than replacing them. While this paper focuses on thedomain of official statistics, a similar situation can be observed inmany fields: organisations have heavily invested in existingdomain-specific standards. Bridging from these standards to thelinked data universe is a prerequisite for getting valuable domainspecific data into the linked data web.
In this paper, we'll first describe SDMX and the data model it
uses. We'll then talk about how publishing statistical data as
linked data provides some advantages that aren't realised within
the more traditional SDMX publishing pattern. We'll go on todescribe how the SDMX data model and process model map on tolinked data concepts, and thus how organisations such as the ONS
can publish statistical data as linked data without disrupting their
existing tool chains.

2. SDMXThe Statistical Data and Metadata Exchange (SDMX) Initiative
was organised in 2001 by seven international organisations (BIS,
ECB, Eurostat, IMF, OECD, World Bank and the UN) who
remain the governing sponsors. The goals for the initiative were to
realise greater efficiencies in statistical practice, with a focus on
employing current technology to enhance efficiency, improve
quality, and address other challenges. These organisations allcollect significant amounts of data, mostly from the national level,to support policy. They also disseminate data at the supra-national
and international levels.

Bank for International Settlements, the OECD, the UN (for theMillenium Development Goals indicators), the World HealthOrganization, UNESCO (for education statistics), the IMF, theWorld Bank, the Food and Agriculture Organization, and manyothers, including numerous national-level statistical organisations
and central banks. In some cases, it has become a standard
mechanism for the dissemination of statistical data and metadata(OECD.stat is a good example). In others, it is used as an internal
standard for processing, or as a means of supporting datacollection and production. Adoption has been increasing rapidly
throughout the official statistical world.
SDMX emphasises the SDMX Information Model shown at a highlevel in Figure 1 - a meta-model of the important aspects ofcollection, processing, exchange, and dissemination of aggregate
data. All of the technology artefacts of SDMX are
implementations of the SDMX Information Model. The model isbased on an earlier standard, GESMES/TS, which used the
UN/EDIFACT flat-file syntax. SDMX in its current version has
expanded this model to include a view of the entire process ofstatistical production. This model is the result of implementation
and analysis of statistical processes in many national, supranational, and international organisations, and has been effectively
used to support these functions in many implementations.

There have been several important results from this work: twoversions of a set of technical specifications - ISO:TS 17369(SDMX) - and the release of several recommendations forstructuring and harmonising cross-domain statistics, the SDMX
Content-Oriented Guidelines. All of the products are available atwww.sdmx.org. The standards are now being widely adopted
around the world for the collection, exchange, processing, anddissemination of aggregate statistics by official statisticalorganisations. The UN Statistical Commission recommended
SDMX as the preferred standard for statistics in 2007.

Figure 2: Schematic high-level view of data structures in the
SDMX Information Model
The SDMX Technical Specifications describe two major syntaxes:
SDMX-EDI, which uses the flat-file UN/EDIFACT syntax; andSDMX-ML, which is broader in scope and offers XML formats
for many types of statistical data and metadata. In both cases,
users configure the formats to work with the statistical concepts ofimportance to their data and metadata, providing a flexible,
generic basis from which to work, which remains conformant withthe standard model.
As well as specifying the formats for exchanging statistical data,
SDMX defines a services-based architecture centred around thedeployment of queryable web-services and coordination enabledby the uses of an optimised set of registry services.
Figure 1: Schematic high-level view of the SDMX Information
Model
SDMX is currently being employed by several producers ofimportant data sets. To name a few examples, it is being used andadopted by the U.S. Federal Reserve Board, the Federal Reserve
Bank of New York, the European Central Bank, Eurostat, theTools are becoming widely available for working with SDMX, as
freeware, as open-source, and in statistical tools offered bycommercial vendors.

3. STATISTICAL LINKED DATA
Linked data takes a different approach from SDMX. Rather than acentralised repository that can resolve URNs for the discovery ofdatasets, linked data simply uses HTTP URIs so that information
can be found using the usual web architecture. Linked dataemphasises the assignment of identifiers to all the instances in adata model, which in the SDMX case includes:
code lists and codesconcept schemes and concepts

datasets and dataset flowstime series and sections

individual observations

concept, as defined in the Event ontology4. A statistical item is aparticular classification of a time/space region. Dimensions of astatistical item are factors of the corresponding events, attachedthrough the dimension property, pointing to an instance of the
SCOVO Dimension class. This model is easily extensible bydefining new factors and agents pertaining to the actual statistical
data. For example, one can relate to a statistical data item theinstitutional body responsible for it as well as the methodology
used. A Dimension can have a minimum (and respectively amaximum) range value, captured through the min and max
properties.

Identifying each of these resources allows statements to be madeabout them. An individual anomalous observation might beannotated, for example. Datasets can be annotated to indicate theprovenance of the statistics, including how they were collected
and what processing they have been through. Being published as
linked data also means that these items are accessible
programmatically via the web. For example, this means that thedetailed semantics of a particular dimension can be located easily,
simply through resolving its identifier on the web.

Figure 3: SCOVO data model

What's more, statistical datasets can link into the wider web of
data, leading to more powerful ways of filtering and querying
statistical data. For example, statistical observations often
reference the geographical area to which the statistic applies.
Extra analysis can be made possible by resolving information
about that area such as the political make-up of its council, howrural it is or the size of its police force. Similarly, information
about the containment of an area inside others supports theaggregation of statistics for larger areas.

The Statistical Core Vocabulary (depicted in Fig. 3) is defined in
RDF Schema.

The Statistical Core Vocabulary (SCOVO) [3] demonstrates theseprinciples and how they can apply to statistical data. It is alightweight RDF vocabulary for expressing statistical data. Its
relative simplicity allows easy adoption by data producers andconsumers, and it can be combined with other RDF vocabularies
for greater effect. The model is extensible both on the schema andthe instance level for more specialized use cases. SCOVO’s
origins are in the riese (“RDFizing and Interlinking the EuroStat
Data Set Effort”) project [4, 5]. It has also been used to express
statistics about RDF datasets [6, 7], in early efforts to convert UK
government data to RDF2, to publish Italian university statistics3,
and in other contexts.

definition and publication of the structure of a dataset
independent from concrete datadata flows which group together datasets that share the samestructure, for example from different national data providers

definition of “slices” through a dataset, such as an individual
time series or cross-section, for individual annotation

distinctions between dimensions, attributes and measures

SCOVO defines three basic concepts:

4. EXPRESSING SDMX IN RDF
While SCOVO addresses the basic use case of publishingstatistical data in linked data form, its minimalist design islimiting, and it does not support important scenarios that occur instatistical publishing and have led to the development of the
SDMX information model, such as:

There are also features of SDMX which are rightly not addressed
by SCOVO but can be expressed through other vocabularies ordesign patterns, such as:describing code lists, category schemes, and mappings
between them using SKOSa dataset, representing the container of some data, such as atable holding some data in its cells;

a data item, representing a single piece of data (e.g. a cell in atable);

describing metadata and access details about datasets using
Dublin Core and voiD [7]

describing organisations using FOAFa dimension, representing some kind of unit of a single pieceof data (for example a time period, location, etc.)

A statistical dataset in SCOVO is represented by the classscovo:Dataset, which is a SKOS concept [8] in order to allow
hooking into a categorisation scheme. A statistical data item
scovo:Item belongs to a dataset. An Item is subsuming the Event2

http://www.jenitennison.com/blog/node/1383

http://sw.unime.it/loius/info.html

In this section, a mapping from SDMX to RDF is described. It isbased on SCOVO, with extensions that partly borrow fromexisting vocabularies and partly reside in a new SDMX
vocabulary. The task of mapping SDMX to RDF is greatly aided
by the fact that the SDMX standard is separated into an abstract
information model (SDMX-IM) and concrete XML andUN/EDIFACT based syntaxes. We will list main structures of the
SDMX information model (see Fig. 1 and Fig. 2), and sketch their4

http://purl.org/NET/c4dm/event.owl

translation to RDF. The section will conclude with first
implementation experiences.

dimensions allows for a more compact RDF representation ofobservations.

Mapping overview. Fig. 4 provides a high-level overview of the
RDF model. At the core of SDMX is the data structure definition(DSD), which describes the structure, or metamodel, of one ormore statistical datasets. Individual datasets must conform to a
DSD, and are represented by instances of the sdmx:DataSet class.
The sdmx:structure property connects a dataset and its DSD. The
sdmx:DataSet class is defined as a subclass of SCOVO’s
scovo:Dataset class, and also as a subclass of void:Dataset, so
VoiD properties can be used to describe access methods(SPARQL endpoint, RDF dump, etc.) to the data. VoiD covers
much of the same ground as SDMX’s web service based registry
module, which we therefore do not map to RDF.

Figure 5: SDMX Data Structure Definition in RDF

Figure 4: Mapping SDMX to RDF: Overview
Data providers. The organisation that publishes a dataset is given
via the dc:publisher relationship. Organisations are represented as
instances of foaf:Agent. Organisations are also used with thesdmx:maintainer property, which indicates the maintenanceagency of various SDMX artefacts, such as DSDs, code lists, andcategory schemes.
Data flows and provision agreements. Two important scenarios
in official statistics are the periodical publishing of datasets
according to a schedule, and the aggregation of datasets fromdifferent data providers (e.g., European Union national statistics
offices) into a larger collection for central dissemination (e.g.,Eurostat). These scenarios are addressed via sdmx:DataFlow. A
data flow represents a “feed” of datasets that all conform to thesame DSD. Data flows are associated with provision agreements,
which can be understood as commitments from an organisation to
publish datasets into a data flow.
Data structure definition details. A DSD, also known as a key
family in SDMX, describes the metamodel of one or more datasets(see Fig. 5). It defines attributes, measures, and dimensions,collectively called components. Measures name the observablephenomenon, such as income per household. Dimensions identify
what is measured, such as of a particular country at a particulartime. Attributes define metadata about the observations, such as
the method of data collection or the unit of measurement.
Components are coded if possible values come from a pre-definedcode list (such as country), or uncoded otherwise. Code lists are
mapped to a subclass of skos:ConceptScheme.
We represent all components as instances of rdf:Property. We
define subclasses of rdf:Property to indicate the particular kind ofcomponent, as well as whether it is coded, and the particular roleit plays in the DSD (e.g, TimeDimension, PrimaryMeasure).
Compared to SCOVO, the property-based modeling ofDimensions, attributes and measures in SDMX take theirsemantics from concepts. Concepts are items in concept schemes.
By using standard concepts and code lists, data becomescomparable across datasets, DSDs, and providers. Concepts could
be modeled as properties, and could be associated withcomponents using rdfs:subPropertyOf. Instead, we model them as
skos:Concepts, and introduce a new property for associating themwith the component. This takes advantage of the easier
management, wider reusability, and fine-grained mapping featuresof SKOS vocabularies compared to RDFS-defined properties.
Data set details. SDMX offers two approaches to organising thedata inside a dataset. Either the dataset is a collection of timeseries (a set of observations that share the same dimension values
except for the time dimension), or it is a collection of crosssections (a set of observations that share the same dimension
values except for one or more non-time “wildcard dimensions”).
In our RDF mapping, we unify both models into a simpler yet
more verbose model that can be more easily interrogated withSPARQL queries (see Fig. 6). The observation values aremodeled as instances of sdmx:Observation, a subclass ofscovo:Item. Each observation instance is directly connected to thesdmx:DataSet via the sdmx:dataset property. An observation musthave a value for each dimension property defined in the DSD. The
actual observation value is recorded using rdf:value.
The time series and cross-sections found in SDMX data are stilltranslated to RDF, in order to make any metadata attached to themavailable in the RDF view. The same applies to groups, which areanother organisational tool that can be used to apply metadata to
sections of a dataset, for example to monthly, quarterly and annual
timelines of the same measure.
Content-Oriented Guidelines. A key component of the SDMX
standards package are the Content-Oriented Guidelines, a set ofcross-domain concepts, code lists, and categories that support
interoperability and comparability between datasets by providing
a shared language between SDMX implementers. We have
performed an initial mapping of the cross-domain concepts andthe category scheme to RDF, although the result has not yet founda permanent home where we can guarantee stable URIs.

6. ACKNOWLEDGEMENTS
This paper is based on the collaboration that was initiated in aworkshop Publishing statistical datasets in SDMX and thesemantic web hosted by ONS in Sunningdale, United Kingdom in
February 2010. The completion of a draft reference model was
one of several recommendations made by the participants, and thisongoing work continues in an open collaborative environment 5.
Taken together with the proposed collaboration to create arecommended style for URI design for use in APIs to find, obtainand query statistical data6, we believe this work represents a key
step towards bringing the worlds of linked data and official
statistics together through the wider adoption of open standards.
The authors would like to thank all the participants at thatworkshop for their input into this work.
The authors would also like to thank John Sheridan for hiscomments and suggestions on an earlier draft of this paper.

7. REFERENCES
Figure 6: SDMX DataSet in RDF
Implementation experience. This mapping from SDMX to RDF
can be carried out in a number of ways. For example, we haveused XSLT to demonstrate mapping SDMX-ML (the XML
dialect for SDMX) into RDF/XML. The mapping is easy toarticulate, and the resulting linked data can be queried in a
number of ways, to provide slices through the data that were notanticipated by the original publishers. On the down side, thisapproach to modeling statistical data does result in a large
numbers of triples, with each observation resulting in a number ofstatements equal to at least the number of dimensions in the DSD,
and consequently large file sizes.

[1] C. Bizer, T. Heath, T. Berners-Lee: Linked Data – The Story
so Far. In International Journal on Semantic Web andInformation Systems (IJSWIS), 2009[2] International Organisation for Standardisation: ISO/TS
17369:2005 Statistical Data and Metadata Exchange(SDMX)[3] M. Hausenblas, W. Halb, Y. Raimond, L. Feigenbaum, D.
Ayers. SCOVO: Using Statistics on the Web of Data.
Proceedings of ESWC 2009 - 6th European Semantic Web
Conference, p704-718, Heraklion, Greece, 2009[4] W. Halb, Y. Raimond, and M. Hausenblas. Building Linked
Data For Both Humans and Machines. In WWW 2008
Workshop: Linked Data on the Web (LDOW2008), Beijing,
China, 2008.

5. CONCLUSIONSThe statistical publishing community and the linked data
community share some common aims. Both seek to make dataeasy to locate and open it up for reuse, particularly for analysis
and visualisation. From a linked data perspective, the world ofstatistical publishing is an extremely rich seam of data. For
statistical publishers, linked data provides a way of addressing
and retrieving information at a range of levels and in a distributed
fashion.
By showing how to map from the standard SDMX information
model into RDF, we hope to illustrate the ease with which
statistical publishers could transition to providing data to bothcommunities. There are already moves within the UK to create
demonstrators that illustrate both the feasibility and the practicalcosts and benefits of publishing statistical data using linked data
principles using this mapping, both from statistical data that iscurrently represented in SDMX and that represented in otherforms such as LGDx and CSV.
Future work in this area includes formally capturing both theadditional RDF vocabularies and concept schemes that are
required to support SDMX and the ways in which this can workwith other vocabularies to provide a mapping from SDMX. We
will also be exploring the use of APIs aimed at web developers
who will not typically be interested in learning the complexities of
either SDMX or, indeed, RDF, but who simply want to accessstatistical data in simple and familiar ways. We believe thatcombining the experience and rigour behind SDMX with the webbased paradigm of linked data in this way will ultimately enhancethe value both of statistical data and of the web of data.

[5] M. Hausenblas, W. Halb, and Y. Raimond. Scripting User
Contributed Interlinking. In 4th Workshop on Scripting for
the Semantic Web (SFSW08), Tenerife, Spain, 2008.

[6] A. Langegger, W. Wöß: RDFStats - An Extensible RDF
Statistics Generator and Library. In DEXA Workshops 2009:
79-83[7] K. Alexander, R. Cyganiak, M. Hausenblas: J. Zhao:
Describing Linked Datasets. In: Proceedings of the Linked
Data on the Web Workshop (LDOW2009), Madrid, Spain,
2009[8] Semantic Web Deployment Working Group. SKOS Simple
Knowledge Organization System Reference. W3C
Recommendation, Semantic Web Deployment Working
Group, 2009.

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