The RDF Data Cube Vocabulary

1.1 A Data Cube vocabulary

Statistical data is a foundation for policy prediction, planning and adjustments and underpins many of the mash-ups and visualisations we see on the web. There is strong interest in being able to publish statistical data in a web-friendly format to enable it to be linked and combined with related information.

At the heart of a statistical dataset is a set of observed values organized along a group of dimensions, together with associated metadata. The Data Cube vocabulary enables such information to be represented using the the W3C RDF (Resource Description Framework) standard and published following the principles of linked data. The vocabulary is based upon the approach used by the SDMX ISO standard for statistical data exchange. This cube model is very general and so the Data Cube vocabulary can be used for other data sets such as survey data, spreadsheets and OLAP data cubes [OLAP].

The Data Cube vocabulary is focused purely on the publication of multi-dimensional data on the web. We envisage a series of modular vocabularies being developed which extend this core foundation. In particular, we see the need for an SDMX extension vocabulary to support the publication of additional context to statistical data (such as the encompassing Data Flows and associated Provision Agreements). Other extensions are possible to support metadata for surveys (so called "micro-data", as encompassed by DDI) or publication of statistical reference metadata.

The Data Cube in turn builds upon the following existing RDF vocabularies:

• SKOS for concept schemes
• SCOVO for core statistical structures
• VoiD for data access
• FOAF for organisations
• Dublin Core Terms for metadata

1.2 RDF and Linked Data

Linked data is an approach to publishing data on the web, enabling datasets to be linked together through references to common concepts. The approach [LOD] recommends use of HTTP URIs to name the entities and concepts so that consumers of the data can look-up those URIs to get more information, including links to other related URIs. RDF [RDF-PRIMER] provides a standard for the representation of the information that describes those entities and concepts, and is returned by dereferencing the URIs.

There are a number of benefits to being able to publish multi-dimensional data, such as statistics, using RDF and the linked data approach:

• The individual observations, and groups of observations, become (web) addressable. This allows publishers and third parties to annotate and link to this data; for example a report can reference the specific figures it is based on allowing for fine grained provenance trace-back.
• Data can be flexibly combined across datasets and between statistical and non-statistical sets (for example find all Religious schools in census areas with high values for National Indicators pertaining to religious tolerance). The statistical data becomes an integral part of the broader web of linked data.
• For publishers who currently only offer static files then publishing as linked-data offers a flexible, non-proprietary, machine readable means of publication that supports an out-of-the-box web API for programmatic access.
• It enables reuse of standardized tools and components.

1.3 SDMX and related standards

The 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) to realise greater efficiencies in statistical practice. These organisations all collect significant amounts of data, mostly from the national level, to support policy. They also disseminate data at the supra-national and international levels.

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

The SDMX specification defines a core information model which is reflected in concrete form in two syntaxes - SDMX-ML (an XML syntax) and SDMX-EDI. The Data Cube vocabulary builds upon the core of the SDMX information model.

A key component of the SDMX standards package are the Content-Oriented Guidelines (COGs), a set of cross-domain concepts, code lists, and categories that support interoperability and comparability between datasets by providing a shared terminology between SDMX implementers. RDF versions of these terms are available separately for use along with the Data Cube vocabulary.

1.4 Relationship to SCOVO

The Statistical Core Vocabulary (SCOVO) [SCOVO] is a lightweight RDF vocabulary for expressing statistical data. Its relative simplicity allows easy adoption by data producers and consumers, and it can be combined with other RDF vocabularies for greater effect. The model is extensible both on the schema and the instance level for more specialized use cases.

While SCOVO addresses the basic use case of expressing statistical data in RDF, its minimalist design is limiting, and it does not support important scenarios that occur in statistical publishing, such as:

• definition and publication of the structure of a dataset independent from concrete data,
• data flows which group together datasets that share the same structure, 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.

The design of the Data Cube vocabulary is informed by SCOVO, and every SCOVO dataset can be re-expressed within the vocabulary.

1.5 Audience and scope

This document describes the Data Cube vocabulary It is aimed at people wishing to publish statistical or other multi-dimension data in RDF. Mechanics of cross-format translation from other formats such as SDMX-ML will be covered elsewhere.

1.6 Document conventions

The names of RDF entities -- classes, predicates, individuals -- are URIs. These are usually expressed using a compact notation where the name is written prefix:localname, and where the prefix identifies a namespace URI. The namesapce identified by the prefix is prepended to the localname to obtain the full URI.

In this document we shall use the conventional prefix names for the well-known namespaces:

• rdf, rdfs -- the core RDF namespaces
• dc -- Dublin Core
• skos -- Simple Knowledge Organization System
• foaf -- Friend Of A Friend
• void -- Vocabulary of Interlinked Datasets
• scovo -- Statistical Core Vocabulary

We also introduce the prefix qb for the Data Cube namespace http://purl.org/linked-data/cube#.

All RDF examples are written in Turtle syntax [TURTLE-TR].

2.1 The cube model - dimensions, attributes, measures

A statistical data set comprises a collection of observations made at some points across some logical space. The collection can be characterized by a set of dimensions that define what the observation applies to (e.g. time, area, gender) along with metadata describing what has been measured (e.g. economic activity, population), how it was measured and how the observations are expressed (e.g. units, multipliers, status). We can think of the statistical data set as a multi-dimensional space, or hyper-cube, indexed by those dimensions. This space is commonly referred to as a cube for short; though the name shouldn't be taken literally, it is not meant to imply that there are exactly three dimensions (there can be more or fewer) nor that all the dimensions are somehow similar in size.

A cube is organized according to a set of dimensions, attributes and measures. We collectively call these components.

The dimension components serve to identify the observations. A set of values for all the dimension components is sufficient to identify a single observation. Examples of dimensions include the time to which the observation applies, or a geographic region which the observation covers.

The measure components represent the phenomenon being observed.

The attribute components allow us to qualify and interpret the observed value(s). They enable specification of the units of measures, any scaling factors and metadata such as the status of the observation (e.g. estimated, provisional).

2.2 Slices

It is frequently useful to group subsets of observations within a dataset. In particular to fix all but one (or a small subset) of the dimensions and be able to refer to all observations with those dimension values as a single entity. We call such a selection a slice through the cube. For example, given a data set on regional performance indicators then we might group all the observations about a given indicator and a given region into a slice, each slice would then represent a time series of observed values.

A data publisher may identify slices through the data for various purposes. They can be a useful grouping to which metadata might be attached, for example to note a change in measurement process which affects a particular time or region. Slices also enable the publisher to identify and label particular subsets of the data which should be presented to the user - they can enable the consuming application to more easily construct the appropriate graph or chart for presentation.

In statistical applications it is common to work with slices in which a single dimension is left unspecified. In particular, to refer to such slices in which the single free dimension is time as Time Series and to refer slices along non-time dimensions as Sections. Within the Data Cube vocabulary we allow arbitrary dimensionality slices and do not give different names to particular types of slice but extension vocabularies, such as SDMX-RDF, can easily add such concept labels.

4.1 Vocabulary index

Classes: qb:Attachable qb:AttributeProperty qb:CodedProperty qb:ComponentProperty qb:ComponentSet qb:ComponentSpecification qb:DataSet qb:DataStructureDefinition qb:DimensionProperty qb:MeasureProperty qb:Observation qb:Slice qb:SliceKey

Properties: qb:attribute qb:codeList qb:component qb:componentAttachment qb:componentProperty qb:componentRequired qb:concept qb:dataSet qb:dimension qb:measure qb:measureDimension qb:measureType qb:observation qb:order qb:slice qb:sliceKey qb:sliceStructure qb:structure qb:subSlice

5.1 Dimensions, attributes and measures

The Data Cube vocabulary represents the dimensions, attributes and measures as RDF properties. Each is an instance of the abstract qb:ComponentProperty class, which in turn has sub-classes qb:DimensionProperty, qb:AttributeProperty and qb:MeasureProperty.

A component property encapsulates several pieces of information:

• the concept being represented (e.g. time or geographic area),
• the nature of the component (dimension, attribute or measure) as represented by the type of the component property,
• the type or code list used to represent the value.

The same concept can be manifested in different components. For example, the concept of currency may be used as a dimension (in a data set dealing with exchange rates) or as an attribute (when describing the currency in which an observed trade took place). The concept of time is typically used only as a dimension but may be encoded as a data value (e.g. an xsd:dateTime) or as a symbolic value (e.g. a URI drawn from the reference time URI set developed by data.gov.uk). In statistical agencies it is common to have a standard thesaurus of statistical concepts which underpin the components used in multiple different data sets.

To support this reuse of general statistical concepts the data cube vocabulary provides the qb:concept property which links a qb:ComponentProperty to the concept it represents. We use the SKOS vocabulary [SKOS-PRIMER] to represent such concepts. This is very natural for those cases where the concepts are already maintained as a controlled term list or thesaurus. When developing a data structure definition for an informal data set there may not be an appropriate concept already. In those cases, if the concept is likely to be reused in other guises it is recommended to publish a skos:Concept along with the specific qb:ComponentProperty. However, if such reuse is not expected then it is not required to do so - the qb:concept link is optional and a simple instance of the appropriate subclass of qb:ComponentProperty is sufficient.

The representation of the possible values of the component is described using the rdfs:range property of the component in the usual RDF manner. Thus, for example, values of a time dimension might be represented using literals of type xsd:dateTime or as URIs drawn from a time reference service.

In statistical data sets it is common for values to be encoded using some (possibly hierarchical) code list and it can be useful to be able to easily identify the overall code list in some more structured form. To cater for this a component can also be optionally annotated with a qb:codeList denoting a skos:ConceptScheme. In such a case the rdfs:range of the component might be left as simply skos:Concept but a useful design pattern is to also define an rdfs:Class whose members are all the skos:Concepts within a particular scheme. In that way the rdfs:range can be made more specific which enables generic RDF tools to perform appropriate range checking.

Note that in the SDMX extension vocabulary there is one further item of information to encode about components - the role that they play within the structure definition. In particular, is sometimes convenient for consumers to be able to easily identify which is the time dimension, which component is the primary measure and so forth. It turns out that such roles are intrinsic to the concepts and so this information is encoded by providing subclasses of skos:Concept for each role. The particular choice of roles here is specific to the SDMX standard and so is not included within the core data cube vocabulary. In cases where such roles are appropriate then we encourage applications of the data cube vocabulary to also supply the relevant SDMX-derived role information.

Before illustrating the components needed for our running example, there is one more piece of machinery to introduce, a reusable set of concepts and components based on SDMX.

5.2 Content oriented guidelines

The SDMX standard includes a set of content oriented guidelines (COG) [COG] which define a set of common statistical concepts and associated code lists that are intended to be reusable across data sets. As part of the data cube work we have created RDF analogues to the COG. These include:

• sdmx-concept: SKOS Concepts for each COG defined concept;
• sdmx-code: SKOS Concepts and ConceptSchemes for each COG defined code list;
• sdmx-dimension: component properties corresponding to each COG concept that can be used as a dimension;
• sdmx-attribute: component properties corresponding to each COG concept that can be used as a attribute;
• sdmx-measure: component properties corresponding to each COG concept that can be used as a measure.

The data cube vocabulary is standalone and it is not mandatory to use the SDMX COG-derived terms. However, when the concepts being expressed do match a COG concept it is recommended that publishers should reuse the corresponding components and/or concept URIs to simplify comparisons across data sets. Given this background we will reuse the relevant COG components in our worked example.

5.3 Example

Turning to our example data set then we can see there are three dimensions to represent - time period, region (unitary authority) and sex of the population. There is a single (primary) measure which corresponds to the topic of the data set (life expectancy) and encodes a value in years. Hence, we need the following components.

Time. There is a suitable predefined concept in the SMDX-COG for this, REF_PERIOD, so we could reuse the corresponding component property sdmx-dimension:refPeriod. However, to represent the time period itself it would be convenient to use the data.gov.uk reference time service and to declare this within the data structure definition.

  eg:refPeriod  a rdf:Property, qb:DimensionProperty;
      rdfs:label "reference period"@en;
      rdfs:subPropertyOf sdmx-dimension:refPeriod;
      rdfs:range interval:Interval;
      qb:concept sdmx-concept:refPeriod . 

Region. Again there is a suitable COG concept and associated component that we can use for this, and again we can customize the range of the component. In this case we can use the Ordanance Survey administrative geography ontology [OS-GEO].

  eg:refArea  a rdf:Property, qb:DimensionProperty;
      rdfs:label "reference area"@en;
      rdfs:subPropertyOf sdmx-dimension:refArea;
      rdfs:range admingeo:UnitaryAuthority;
      qb:concept sdmx-concept:refArea . 

Sex. In this case we can use the corresponding COG component sdmx-dimension:sex directly, since the default code list for it includes the terms we need.

Measure. This property will give the value of each observation. We could use the default smdx-measure:obsValue for this (defining the topic being observed using metadata). However, it can aid readability and processing of the RDF data sets to use a specific measure corresponding to the phenomenon being observed.

  eg:lifeExpectancy  a rdf:Property, qb:MeasureProperty;
      rdfs:label "life expectancy"@en;
      rdfs:subPropertyOf sdmx-measure:obsValue;
      rdfs:range xsd:decimal . 

Unit measure attribute. The primary measure on its own is a plain decimal value. To correctly interpret this value we need to define what units it is measured in (years in this case). This is defined using attributes which qualify the interpretation of the observed value. Specifically in this example we can use the predefined sdmx-attribute:unitMeasure which in turn corresponds to the COG concept of UNIT_MEASURE. To express the value of this attribute we would typically us a common thesaurus of units of measure. For the sake of this simple example we will use the DBpedia resource http://dbpedia.org/resource/Year which corresponds to the topic of the Wikipedia page on "Years".

This covers the minimal components needed to define the structure of this data set.

5.4 ComponentSpecifications and DataStructureDefinitions

To combine the components into a specification for the structure of this dataset we need to declare a qb:DataStuctureDefinition resource which in turn will reference a set of qb:ComponentSpecification resources. The qb:DataStuctureDefinition will be reusable across other data sets with the same structure.

In the simplest case the qb:ComponentSpecification simply references the corresponding qb:ComponentProperty (ususally using one of the sub properties qb:dimension, qb:measure or qb:attribute). However, it is also possible to qualify the component specification in several ways.

• An Attribute may be optional in which case the specification should set qb:componentRequired "false"^^xsd:boolean.
• The components may be ordered by giving an integer value for qb:order. This order carries no semantics but can be useful to aid consuming agents in generating appropriate user interfaces. It can also be useful in the publication chain to enable synthesis of appropriate URIs for observations.
• By default the values of all of the components will be attached to each individual observation, a so called flattened representation. This allows such observations to stand alone, so that a SPARQL query to retrieve the observation can immediately locate the attributes which enable the observation to be interpreted. However, it is also permissible to attach attributes to the overall data set, to an intervening slice or to a specific Measure (in the case of multiple measures). This reduces some of the redundancy in the encoding of the instance data. To declare such a non-flat structure, the qb:componentAttachment property of the specification should reference the class corresponding to the attachment level (e.g. qb:DataSet for attributes that will be attached to the overall data set).

In the case of our running example the dimensions can be usefully ordered. There is only one attribute, the unit measure, and this is required. In the interests of illustrating the vocabulary use we will declare that this attribute will be attached at the level of the data set, however flattened representations are in general easier to query and combine.

So the structure of our example data set (and other similar datasets) can be declared by:

  eg:dsd-le a qb:DataStructureDefinition;
      # The dimensions
      qb:component [qb:dimension eg:refArea;         qb:order 1];
      qb:component [qb:dimension eg:refPeriod;       qb:order 2];
      qb:component [qb:dimension sdmx-dimension:sex; qb:order 3];
      # The measure(s)
      qb:component [qb:measure eg:lifeExpectancy];
      # The attributes
      qb:component [qb:attribute sdmx-attribute:unitMeasure; qb:componentAttachment qb:DataSet;] .

Note that we have given the data structure definition (DSD) a URI since it will be reused across different datasets with the same structure. Similarly the component properties themselves can be reused across different DSDs. However, the component specifications are only useful within the scope of a particular DSD and so we have chosen the represent them using blank nodes.

5.5 Handling multiple measures

Our example data set is relatively simple in having a single observable (in this case "life expectancy") that is being measured. In other data sets there can be multiple measures. These measures may be of similar nature (e.g. a data set on local government performance might provide multiple different performance indicators for each region) or quite different (e.g. a data set on trades might provide quantity, value, weight for each trade).

There are two approaches to representing multiple measures. In the SDMX information model, each observation can record a single observed value. In a data set with multiple observations then we add an additional dimension whose value indicates the measure. This is appropriate for applications where the measures are separate aggregate statistics. In other domains such as a clinical statistics or sensor networks then the term observation usually denotes an observation event which can include multiple observed values. Similarly in Business Intelligence applications and OLAP, a single "cell" in the data cube will typically contain values for multiple measures.

The data cube vocabulary permits either representation approach to be used though they cannot be mixed within the same data set.

eg:dsd1 a qb:DataStructureDefinition;
    rdfs:comment "shipments by time (multiple measures approach)"@en;
    qb:component 
        [ qb:dimension  sdmx-dimension:refTime; ],
        [ qb:measure    eg-measure:quantity; ],
        [ qb:measure    eg-measure:weight; ] . 

eg:dataset1 a qb:DataSet;
    qb:structure eg:dsd1 .
    
eg:obs1a  a qb:Observation;
    qb:dataSet eg:dataset1;
    sdmx-dimension:refTime "30-07-2010"^^xsd:date;
    eg-measure:weight 1.3 ;
    eg-measure:quantity 42 ;
    . 

eg:dsd2 a qb:DataStructureDefinition;
    rdfs:comment "shipments by time (measure dimension approach)"@en;
    qb:component 
        [ qb:dimension  sdmx-dimension:refTime; ],
        [ qb:measure    eg-measure:quantity; ],
        [ qb:measure    eg-measure:weight; ],
        [ qb:dimension  qb:measureType; ] . 

eg:dataset2 a qb:DataSet;
    qb:structure eg:dsd2 .
    
eg:obs2a  a qb:Observation;
    qb:dataSet eg:dataset2;
    sdmx-dimension:refTime "30-07-2010"^^xsd:date;
    qb:measureType eg-measure:weight ;
    eg-measure:weight 1.3 .
    
eg:obs2b  a qb:Observation;
    qb:dataSet eg:dataset2;
    sdmx-dimension:refTime "30-07-2010"^^xsd:date;
    qb:measureType eg-measure:quantity ;
    eg-measure:quantity 42 . 

6.1 Data sets and observations

A resource representing the entire data set is created and typed as qb:DataSet and linked to the corresponding data structure definition via the qb:structure property.

Pitfall: Note the capitalization of qb:DataSet, which differs from the capitalization in other vocabularies, such as void:Dataset and dcat:Dataset. This unusual capitalization is chosen for compatibility with the SDMX standard. The same applies to the related property qb:dataSet.

Each observation is represented as an instance of type qb:Observation. In the basic case then values for each of the attributes, dimensions and measurements are attached directly to the observation (remember that these components are all RDF properties). The observation is linked to the containing data set using the qb:dataSet property. For example:

Thus for our running example we might expect to have:

  eg:dataset-le1 a qb:DataSet;
      rdfs:label "Life expectancy"@en;
      rdfs:comment "Life expectancy within Welsh Unitary authorities - extracted from Stats Wales"@en;
      qb:structure eg:dsd-le ;
      .  

  eg:o1 a qb:Observation;
      qb:dataSet  eg:dataset-le1 ;
      eg:refArea                 admingeo:newport_00pr ;                  
      eg:refPeriod               <http://reference.data.gov.uk/id/gregorian-interval/2004-01-01T00:00:00/P3Y> ;
      sdmx-dimension:sex         sdmx-code:sex-M ;
      sdmx-attribute:unitMeasure <http://dbpedia.org/resource/Year> ;
      eg:lifeExpectancy          76.7 ;
      .

  eg:o2 a qb:Observation;
      qb:dataSet  eg:dataset-le1 ;
      eg:refArea                 admingeo:cardiff_00pt ;                  
      eg:refPeriod               <http://reference.data.gov.uk/id/gregorian-interval/2004-01-01T00:00:00/P3Y> ;
      sdmx-dimension:sex         sdmx-code:sex-M ;
      sdmx-attribute:unitMeasure <http://dbpedia.org/resource/Year> ;
      eg:lifeExpectancy          78.7 ;
      .

  eg:o3 a qb:Observation;
      qb:dataSet  eg:dataset-le1 ;
      eg:refArea                 admingeo:monmouthshire_00pp ;                  
      eg:refPeriod               <http://reference.data.gov.uk/id/gregorian-interval/2004-01-01T00:00:00/P3Y> ;
      sdmx-dimension:sex         sdmx-code:sex-M ;
      sdmx-attribute:unitMeasure <http://dbpedia.org/resource/Year> ;
      eg:lifeExpectancy          76.6 ;
      .

  ...

This flattened structure makes it easy to query and combine data sets but there is some redundancy here. For example, the unit of measure for the life expectancy is uniform across the whole data set and does not change between observations. To cater for situations like this the Data Cube vocabulary allows components to be attached at a high level in the nested structure. Indeed if we re-examine our original Data Structure Declaration we see that we declared the unit of measure to be attached at the data set level. So the corrected example is:

  eg:dataset-le1 a qb:DataSet;
      rdfs:label "Life expectancy"@en;
      rdfs:comment "Life expectancy within Welsh Unitary authorities - extracted from Stats Wales"@en;
      qb:structure eg:dsd-le ;  
      sdmx-attribute:unitMeasure <http://dbpedia.org/resource/Year> ;
      .
      
  eg:o1 a qb:Observation;
      qb:dataSet  eg:dataset-le1 ;
      eg:refArea                 admingeo:newport_00pr ;                  
      eg:refPeriod               <http://reference.data.gov.uk/id/gregorian-interval/2004-01-01T00:00:00/P3Y> ;
      sdmx-dimension:sex         sdmx-code:sex-M ;
      eg:lifeExpectancy          76.7 ;
      .
      
  eg:o2 a qb:Observation;
      qb:dataSet  eg:dataset-le1 ;
      eg:refArea                 admingeo:cardiff_00pt ;                  
      eg:refPeriod               <http://reference.data.gov.uk/id/gregorian-interval/2004-01-01T00:00:00/P3Y> ;
      sdmx-dimension:sex         sdmx-code:sex-M ;
      eg:lifeExpectancy          78.7 ;
      .

  eg:o3 a qb:Observation;
      qb:dataSet  eg:dataset-le1 ;
      eg:refArea                 admingeo:monmouthshire_00pp ;                  
      eg:refPeriod               <http://reference.data.gov.uk/id/gregorian-interval/2004-01-01T00:00:00/P3Y> ;
      sdmx-dimension:sex         sdmx-code:sex-M ;
      eg:lifeExpectancy          76.6 ;
      .

  ...

In a data set containing just observations with no intervening structure then each observation must have a complete set of dimension values, along with all the measure values. If the set is structured by using slices then further abbreviation is possible, as discussed in the next section.

9.1 Categorizing a data set

Publishers of statistics often categorize their data sets into different statistical domains, such as Education, Labour, or Transportation. We encourage use of dcterms:subject to record such a classification of a whole data set. The classification terms can include coarse grained classifications, such as the List of Subject-matter Domains from the SDMX Content-oriented Guidelines, and fine grained classifications to support discovery of data sets.

The classification schemes are typically represented using the SKOS vocabulary. For convenience the SMDX Subject-matter Domains have been encoded as a SKOS concept scheme at http://purl.org/linked-data/sdmx/2009/subject#.

Thus our sample dataset might be marked up by:

  eg:dataset1 a qb:DataSet;
      rdfs:label "Life expectancy"@en;
      rdfs:comment "Life expectancy within Welsh Unitary authorities - extracted from Stats Wales"@en;
      dcterms:date "2010-08-11"^^xsd:date;
      dcterms:subject
          sdmx-subject:3.2 ,      # regional and small area statistics
          sdmx-subject:1.4 ,      # Health
          admingeo:wales_gor_l ;  # Wales
      ...

where eg:Wales is a skos:Concept drawn from an appropriate controlled vocabulary for places.

9.2 Describing publishers

The organization that publishes a dataset should be recorded as part of the dataset metadata. Again we recommend use of the Dublin Core term dcterms:publisher for this. The organization should be represented as an instance of foaf:Agent, or some more specific subclass such as org:Organization [ORG].

eg:dataset1 a qb:DataSet;
    dc:publisher <http://example.com/meta#organization> .
    
<http://example.com/meta#organization> a org:Organization, foaf:Agent;
    rdfs:label "Example org" .    

Note that the SDMX extension vocabulary supports further description of publication pipelines (data flows, reporting taxonomies, maintainers, provision agreements).