1.Introduction
TheData on the Web Best Practices have pointed out the relevance ofpublishing information about the quality of data published on the Web. Accordingly, theW3C Data on the Web Best Practices Working Group has been chartered to create a vocabulary for expressing data quality. The Data Quality Vocabulary (DQV) presented in this document is foreseen as an extension to the DCAT vocabulary [vocab-dcat] to cover the quality of the data, how frequently is it updated, whether it accepts user corrections, persistence commitments etc. When used by publishers, this vocabulary will foster trust in the data amongst developers.
This vocabulary does not seek to determine what "quality" means. We believe that quality lies in the eye of the beholder; that there is no objective, ideal definition of it. Some datasets will be judged as low-quality resources by some data consumers, while they will perfectly fit others' needs. In accordance, we attach a lot of importance to allowing many actors to assess the quality of datasets and publish their annotations, certificates, opinions about a dataset. A dataset's publisher should seek to publish metadata that helps data consumers determine whether they can use the dataset to their benefit. However, publishers should not be the only ones to have a say on the quality of data published in an open environment like the Web. Certification agencies, data aggregators, data consumers can make relevant quality assessments, too.
We want to stimulate this by making it easier to publish, exchange and consume quality metadata, for every step of a dataset's lifecycle. This is why next to rather expected constructs like quality measurements, the Data Quality Vocabulary puts an emphasis on feedback, annotation, policies and certificates.
DQV draws inspiration from and is aligned with existing work on representing data quality, notably the daQ ontology for representing information (especially metrics) on the quality of linked open datasets [DaQ]. daQ is itself anchored in the RDF Data Cube framework for publishing statistical data [Vocab-Data-Cube]. Note that DQV elements can be applied not only to express metadata on the quality of datasets; they can also be used to express statements about the quality of that metadata itself. This is especially true when it comes to representing the provenance of that metadata or its conformance with respect to established metadata standards.
A list of DQV-related implementations including references to vocabulary extending DQV, tools serializing quality results in DQV, portals exposing data quality in DQV, and papers citing the DQV, is maintained at the DQV Implementation wiki page. Please feel free to contact the editors if you have any implementation to report.
6.Example Usage
This section shows some examples to illustrate the application of the Dataset Quality Vocabulary.NB: in the remainder of this section, the prefix ":" refers tohttp://example.org/
6.1Express a quality assessment with quality metrics
Let us consider a dataset:myDataset , and its distribution:myDatasetDistribution ,:myDataset a dcat:Dataset ; dcterms:title "My dataset" ; dcat:distribution :myDatasetDistribution .:myDatasetDistribution a dcat:Distribution ; dcat:downloadURL <http://www.example.org/files/mydataset.csv> ; dcterms:title "CSV distribution of dataset" ; dcat:mediaType "text/csv" ; dcat:byteSize "87120"^^xsd:decimal .
An automated quality checker has provided a quality assessment with two (CSV) quality measurements for:myDatasetDistribution .
:myDatasetDistribution dqv:hasQualityMeasurement :measurement1, :measurement2 .:measurement1 a dqv:QualityMeasurement ; dqv:computedOn :myDatasetDistribution ; dqv:isMeasurementOf :downloadURLAvailabilityMetric ; dqv:value "true"^^xsd:boolean .:measurement2 a dqv:QualityMeasurement ; dqv:computedOn :myDatasetDistribution ; dqv:isMeasurementOf :csvCompletenessMetric ; dqv:value "0.5"^^xsd:double .#definition of dimensions and metrics:availability a dqv:Dimension ; skos:prefLabel "Availability"@en ; skos:definition "Availability of a dataset is the extent to which data (or some portion of it) is present, obtainable and ready for use."@en ; dqv:inCategory :accessibility .:completeness a dqv:Dimension ; skos:prefLabel "Completeness"@en ; skos:definition "Completeness refers to the degree to which all required information is present in a particular dataset."@en ; dqv:inCategory :intrinsicDimensions .:downloadURLAvailabilityMetric a dqv:Metric ; skos:definition "It checks if dcat:downloadURL is available and if its value is dereferenceable."@en ; dqv:expectedDataType xsd:boolean ; dqv:inDimension :availability .:csvCompletenessMetric a dqv:Metric ; skos:definition "Ratio between the number of objects represented in the csv and the number of objects expected to be represented according to the declared dataset scope."@en ; dqv:expectedDataType xsd:double ; dqv:inDimension :completeness .
Categories and dimensions might be more extensively defined, see in the section 'Dimensions and metrics hints' for further examples. Any quality framework is free to define its own dimensions and categories.
6.2Document the provenance of the quality metadata
The results of metrics obtained in the previous assessment are stored in the:myQualityMetadata graph.
# :myQualityMatadata is a graph :myQualityMetadata {:myDatasetDistribution dqv:hasQualityMeasurement :measurement1, :measurement2 .# The graph contains the rest of the statements presented in the previous example.}# :myQualityMetadata has been created by :myQualityChecker and it is the result of the# :myQualityChecking activity :myQualityMetadata a dqv:QualityMetadata ; prov:wasAttributedTo :myQualityChecker ; prov:generatedAtTime "2015-05-27T02:52:02Z"^^xsd:dateTime ; prov:wasGeneratedBy :myQualityChecking .# :myQualityChecker is a service computing some quality metrics :myQualityChecker a prov:SoftwareAgent ; rdfs:label "A quality assessment service"^^xsd:string . # Further details about quality service/software can be provided, for example, # deploying vocabularies such as Dataset Usage Vocabulary (DUV), Dublin Core or ADMS.SW# :myQualityChecking is the activity that has generated :myQualityMetadata from # :myDatasetDistribution:myQualityChecking a prov:Activity; rdfs:label "The checking of myDatasetDistribution's quality"^^xsd:string; prov:wasAssociatedWith :myQualityChecker; prov:used :myDatasetDistribution; prov:generated :myQualityMetadata; prov:endedAtTime "2015-05-27T02:52:02Z"^^xsd:dateTime; prov:startedAtTime "2015-05-27T00:52:02Z"^^xsd:dateTime .6.3Document the provenance of single quality measurements
The group has discussed provenance at different levels of granularity (dqv:QualityMeasurement and dqv:QualityMetadata). In the previous example we have shown how to track provenance at the level of quality metadata as a whole. In the following, we provide an example of provenance for a single quality measurement,:myMeasurement.
:myDatasetDistribution dqv:hasQualityMeasurement :myMeasurement .# :myMeasurement has been created by :myQualityChecker and it is the result of the# :myQualityChecking activity :myMeasurement a dqv:QualityMeasurement ; dqv:computedOn :myDatasetDistribution ; dqv:isMeasurementOf :downloadURLAvailabilityMetric ; dqv:value "true"^^xsd:boolean ; prov:wasAttributedTo :myQualityChecker ; prov:generatedAtTime "2015-05-27T02:52:02Z"^^xsd:dateTime ; prov:wasGeneratedBy :myQualityChecking .:downloadURLAvailabilityMetric a dqv:Metric ; skos:definition "Checks if dcat:downloadURL is available and if its value is dereferenceable."@en ; dqv:expectedDataType xsd:boolean ; dqv:inDimension :availability . # :myQualityChecker is a service computing some quality metrics :myQualityChecker a prov:SoftwareAgent ; rdfs:label "A quality assessment service"^^xsd:string . # Further details about quality service/software can be provided, for example, # deploying vocabularies such as Dataset Usage Vocabulary (DUV), Dublin Core or ADMS.SW# :myQualityChecking is the activity that has generated :myMeasurement from :myDatasetDistribution:myQualityChecking a prov:Activity; rdfs:label "The checking of myDatasetDistribution's quality"^^xsd:string; prov:wasAssociatedWith :myQualityChecker; prov:used :myDatasetDistribution; prov:generated :myMeasurement; prov:endedAtTime "2015-05-27T02:52:02Z"^^xsd:dateTime; prov:startedAtTime "2015-05-27T00:52:02Z"^^xsd:dateTime .
6.4Document the provenance of a dataset
Statements similar to the ones applied to the resource:myQualityMetadata above can be applied to the resource:myDataset to indicate the provenance of the dataset. I.e., a dataset can be generated by a specific software agent, be generated at a certain time, etc. TheHCLS Community Profile for describing datasets provides further examples.
6.5Express that a dataset received an ODI certificate
Let us express that an ODI certificate for the "City of Raleigh Open Government Data" dataset is available at the URL <https://certificates.theodi.org/en/datasets/393/certificate>.
<https://certificates.theodi.org/en/datasets/393> a dcat:Dataset ; dqv:hasQualityAnnotation :myDatasetQA .:myDatasetQA a dqv:QualityCertificate ; oa:hasTarget <https://certificates.theodi.org/en/datasets/393> ; oa:hasBody <https://certificates.theodi.org/en/datasets/393/certificate> ; oa:motivatedBy dqv:qualityAssessment .
6.6Express a question about dataset quality
Let us ask a question about the completeness of the "City of Raleigh Open Government Data" dataset.
<https://certificates.theodi.org/en/datasets/393> a dcat:Dataset ; dqv:hasQualityAnnotation :questionQA .:questionQA a dqv:UserQualityFeedback ; oa:hasTarget <https://certificates.theodi.org/en/datasets/393> ; oa:hasBody :textBody ; oa:motivatedBy dqv:qualityAssessment, oa:questioning ; dqv:inDimension :completeness .:textBody a oa:TextualBody ; rdf:value "Could you please provide information about the completeness of your dataset?" ; dc:language "en" ; dc:format "text/plain" .
6.7Express that a dataset fits in a quality classification
Let us express that the "City of Raleigh Open Government Data" dataset is classified as a four stars dataset against the 5 Stars Linked Open Data rating system.
<https://certificates.theodi.org/en/datasets/393> a dcat:Dataset ; dqv:hasQualityAnnotation :classificationQA .:classificationQA a dqv:UserQualityFeedback ; oa:hasTarget <https://certificates.theodi.org/en/datasets/393> ; oa:hasBody :four_stars ; oa:motivatedBy dqv:qualityAssessment, oa:classifying ; dqv:inDimension :availability .:four_stars a skos:Concept; skos:inScheme :OpenData5Star ; skos:prefLabel "Four stars"@en ; skos:definition "Dataset available on the Web with structured machine-readable non proprietary format. It uses URIs to denote things."@en .
6.8Express derivation between quality metrics, measurements and annotations
DQV models derivation with the propertyprov:wasDerivedFrom. For example, the accessibility of the dataset:myDataset can be derived from the accessibility of its distributions:myCSVDatasetDistribution and:mySPARQLDatasetDistribution.
:myDataset a dcat:Dataset ; dcterms:title "My dataset" ; dcat:distribution :myCSVDatasetDistribution, :mySPARQLDatasetDistribution .:myCSVDatasetDistribution a dcat:Distribution ; dcat:downloadURL <http://www.example.org/files/mydataset.csv> ; dcterms:title "CSV distribution of dataset" ; dcat:mediaType "text/csv" ; dcat:byteSize "87120"^^xsd:decimal .:mySPARQLDatasetDistribution a dcat:Distribution ; dcat:accessURL <http://www.example.org/sparql> dcterms:title "SPARQL access to the dataset" ; dcat:mediaType "application/sparql-results+json" .# definition of dimensions and metrics:availability a dqv:Dimension ; skos:prefLabel "Availability"@en ; skos:definition "Availability of a dataset is the extent to which data (or some portion of it) is present, obtainable and ready for use."@en ; dqv:inCategory :accessibility .:downloadURLAvailabilityMetric a dqv:Metric ; skos:definition "Checks if dcat:downloadURL is available and if its value is dereferenceable."@en ; dqv:expectedDataType xsd:boolean ; dqv:inDimension :availability .:SPARQLAvailabilityMetric a dqv:Metric ; skos:definition "Checks if an URL specified in dcat:accessURL is available and if at that URL a SPARQL endpoint is active."@en ; dqv:expectedDataType xsd:boolean ; dqv:inDimension :availability . :datasetAvailabilityMetric a dqv:Metric ; prov:wasDerivedFrom :downloadURLAvailabilityMetric, :SPARQLAvailabilityMetric; skos:definition "Checks the availabitity of the specified distributions."@en ; dqv:expectedDataType xsd:boolean ; dqv:inDimension :availability .
Depending on the specific application context, the expression of this derivation can be kept at level of the quality measurements. In the following, the measurement:measurement3 of:myDataset's availability is derived from:measurement1 and:measurement2.
:myCSVDatasetDistribution dqv:hasQualityMeasurement :measurement1 .:mySPARQLDatasetDistribution dqv:hasQualityMeasurement :measurement2 .:myDataset dqv:hasQualityMeasurement :measurement3 .:measurement1 a dqv:QualityMeasurement ; dqv:computedOn :myCSVDatasetDistribution ; dqv:isMeasurementOf :downloadURLAvailabilityMetric ; dqv:value "true"^^xsd:boolean :measurement2 a dqv:QualityMeasurement ; dqv:computedOn :mySPARQLDatasetDistribution ; dqv:isMeasurementOf :SPARQLAvailabilityMetric ; dqv:value "false"^^xsd:boolean . :measurement3 a dqv:QualityMeasurement ; dqv:computedOn :myDataset ; dqv:isMeasurementOf :datasetAvailabilityMetric ; prov:wasDerivedFrom :measurement1, :measurement2 ; dqv:value "false"^^xsd:boolean .
The classification of:myDataset as:three_stars can be derived from the result of a quality measurement:measurement2
:myDataset dqv:hasQualityAnnotation :myDatasetClassification .:myDatasetClassification a dqv:UserQualityFeedback ; prov:wasDerivedFrom :measurement2 ; oa:hasTarget :myDataset ; oa:hasBody :three_stars ; oa:motivatedBy dqv:qualityAssessment, oa:classifying ; dqv:inDimension :availability .:three_stars a skos:Concept; skos:inScheme :OpenData5Star ; skos:prefLabel "three stars"@en ; skos:definition "Dataset available on the Web with structured machine-readable non proprietary format."@en .
6.9Express quality of SKOS concept schemes
Let’s consider:myControlledVocabulary, a controlled vocabulary made available on the Web using the SKOS [SKOS-reference] and DCAT [vocab-dcat].
:myControlledVocabulary a dcat:Dataset ; dcterms:title "My controlled vocabulary" .:myControlledVocabularyDistribution a dcat:Distribution ; dcat:downloadURL <http://www.example.org/files/myControlledVocabulary.ttl> ; dcterms:title "SKOS/RDF distribution of my controlled vocabulary" ; dcat:mediaType "text/turtle" ; dcat:byteSize "190120"^^xsd:decimal .
qSKOS is an open source tool, which detects quality issues affecting SKOS vocabularies [qSKOS]. It considers 26 quality issues including, for example, “Incomplete Language Coverage” and “Label Conflicts” which are grouped in the category “Labeling and Documentation issues”. Quality issues addressed by qSKOS can be considered as DQV quality dimensions, whilst the number of concepts in which a quality issue occurs can be the metric deployed for each quality dimension.
# definition of instances for some of the metrics, dimensions and categories deployed# in qSKOS. :numOfConceptsWithLabelConflicts a dqv:Metric; skos:prefLabel "Conflicting concepts"@en ; skos:definition "Number of concepts having conflicting labels"@en ; dqv:expectedDataType xsd:interger ; dqv:inDimension :LabelConflicts .:numOfConceptsWithIncompleteLanguageCoverage a dqv:Metric; skos:prefLabel "Language incomplete concepts"@en ; skos:definition "Number of concepts having an incomplete language coverage"@en ; dqv:expectedDataType xsd:interger ; dqv:inDimension :incompleteLanguageCoverage .:LabelConflicts a dqv:Dimension; skos:prefLabel "Label Conflicts"@en ; skos:definition "Dimension corresponding to the label conflicts quality issue"@en ; dqv:inCategory :labelingDocumentationIssues .:incompleteLanguageCoverage a dqv:Dimension; skos:prefLabel "Incomplete Language Coverage"@en ; skos:definition "Dimension corresponding to the incomplete language coverage issue"@en ; dqv:inCategory :labelingDocumentationIssues .:labelingDocumentationIssues a dqv:Category ; skos:prefLabel "Labeling and Documentation Issues"@en ; skos:definition "Category grouping labeling and documentation issues"@en .
DQV represents the qSKOS quality assessment on:myControlledVocabulary for the dimensions “Incomplete Language Coverage” and “Label Conflicts”.
:myDatasetDistribution dqv:hasQualityMeasurement :measurement1, :measurement2 .:measurement1 a dqv:QualityMeasurement ; dqv:computedOn :myControlledVocabulary ; dqv:isMeasurementOf :numOfConceptsWithMissingValues ; dqv:value "1500"^^xsd:integer .:measurement2 a dqv:QualityMeasurement ; dqv:computedOn :myControlledVocabulary ; dqv:isMeasurementOf :numOfConceptsWithIncompleteLanguageCoverage ; dqv:value "450"^^xsd:integer .
6.10Express the quality of a linkset
(VoID) linksets are collections of (RDF) links between two datasets: the linkset's subject and the linkset's object, containing respectively the subjects and objects of all link statements. Linksets are as important as datasets when it comes to the joint exploitation of datasets served independently in Linked Data. The representation of quality for a linkset is another example of how DQV can be exploited.
Let’s define three DCAT datasets, including one VoID linkset, which connects a subject dataset to an object dataset named respectively byvoid:subjectsTarget and byvoid:objectsTarget:
:myDataset1 a dcat:Dataset ; dcterms:title "My dataset 1" .:myDataset2 a dcat:Dataset ; dcterms:title "My dataset 2" .:myLinkset a dcat:Dataset, void:Linkset ; dcterms:title "A Linkset from My dataset 1 to My dataset 2"; void:linkPredicate skos:exactMatch ; void:subjectsTarget :myDataset1 ; void:objectsTarget :myDataset2 .
We can represent information about the quality of:myLinkset using the “Multilingual importing” quality metric [MultilingualImporting]. This metric applies to linksets between datasets that include SKOS concepts [SKOS-reference]. It considers the concepts that have been matched and quantifies the information gain the links provide if one adds the preferred labels or the alternative labels of the concepts from the object dataset to the descriptions of the concepts from the subject dataset. Let us first define the proper metric, dimension and category.
# Definition of instances for Metric, Dimension and Category. :importingForPropertyPercentage a dqv:Metric ; skos:definition "Ratio between novel preferred or alternative labels gained from the object dataset via skos:exactMatch links and the preferred or alternative labels that are in the subject and object datasets."@en dqv:expectedDataType xsd:double ; dqv:inDimension :completeness .:completenessGain a dqv:Dimension ; skos:prefLabel "Completeness Gain"@en ; skos:definition "Degree to which a linkset contribute to add relevant information to a particular dataset."@en ; dqv:inCategory :complementationGain .:complementationGain a dqv:Category ; skos:definition "Category that groups dimensions measuring the data quality gain obtained by exploiting linksets."@en .
The quality assessment of the "label importing" depends on two extra parameters::onProperty and:onLanguage, which are respectively, the SKOS property and the language tag considered for measuring the completeness gains. We add the following statements to represent these parameters (note that typeqb:DimensionProperty and the range definitions are firstly needed forcompatibility with RDF Data Cube).
:onLanguage a qb:DimensionProperty, owl:DataProperty ; rdfs:comment "Language on which label importing is assessed."@en ; rdfs:domain dqv:QualityMeasurement; rdfs:label "Label import assessment language"@en ; rdfs:range xsd:string .:onProperty a qb:DimensionProperty, rdf:Property ; rdfs:comment "Property on which label importing is assessed."@en ; rdfs:domain dqv:QualityMeasurement ; rdfs:label "Label import assessment property"@en ; rdfs:range rdf:Property .
Let us add actual quality assessments for alternative and preferred labels in Italian and English:
## for Italian alternative labels :measurement_exactMatchAltLabelIt a dqv:QualityMeasurement; dqv:computedOn :myLinkset ; dqv:value "1.0"^^xsd:double ; dcterms:date "2016-01-10"^^xsd:date ; dqv:isMeasurementOf :importingForPropertyPercentage ; :onLanguage "it" ; :onProperty skos:altLabel .## for English alternative labels:measurement_exactMatchAltLabelEn a dqv:QualityMeasurement; dqv:computedOn :myLinkset ; dqv:value "0.1"^^xsd:double ; dcterms:date "2016-01-10"^^xsd:date; dqv:isMeasurementOf :importingForPropertyPercentage ; :onLanguage "en" ; :onProperty skos:altLabel .## for Italian preferred labels:measurement_exactMatchPrefLabelIt a dqv:QualityMeasurement; dqv:computedOn :myLinkset ; dqv:value "0.5"^^xsd:double ; dcterms:date "2016-01-10"^^xsd:date ; dqv:isMeasurementOf :importingForPropertyPercentage ; :onLanguage "it" ; :onProperty skos:prefLabel .## for English preferred labels:measurement_exactMatchPrefLabelEn a dqv:QualityMeasurement; dqv:computedOn :myLinkset ; dqv:value "1"^^xsd:double ; dcterms:date "2016-01-10"^^xsd:date ; dqv:isMeasurementOf :importingForPropertyPercentage ; :onLanguage "en" ; :onProperty skos:prefLabel .
6.11Express the conformance of a dataset's metadata with a standard
It is often desirable to indicate that metadata about datasets in a catalogue are compliant with a metadata standard, or an application profile of an existing metadata standard. A typical example is the GeoDCAT Application Profile [GeoDCAT-AP], an extension of the DCAT vocabulary [vocab-dcat] to represent metadata for geospatial data portals. GeoDCAT-AP allows one to express that a dataset's metadata conforms to an existing standard, following the recommendations of ISO 19115, ISO 19157 and the EU INSPIRE directive. DCAT partly supports the expression of such metadata conformance statements. The following example illustrates how a(DCAT) catalog record can be said to be conformant with the GeoDCAT-AP standard itself.
:myDataset a dcat:Dataset .:myDatasetRecord a dcat:CatalogRecord ; foaf:primaryTopic :myDataset ; dcterms:conformsTo :geoDCAT-AP .:geoDCAT-AP a dcterms:Standard; dcterms:title "GeoDCAT Application Profile. Version 1.0" ; dcterms:comment "GeoDCAT-AP is developed in the context of the Interoperability Solutions for European Public Administrations (ISA) Programme"@en; dcterms:issued "2015-12-23"^^xsd:date ; foaf:page <https://joinup.ec.europa.eu/asset/dcat_application_profile/asset_release/geodcat-ap-v10> .
Note that this example does not include the metadata about the dataset:myDataset itself. We assume this is present in an RDF data source accessible via the URI:myDatasetRecord. We also assume that:geoDCAT-AP is a reference URI that denotes the GeoDCAT-AP standard, which can be re-used across many catalog record descriptions, not just a locally introduced URI.
Note
Finer-grained representation of conformance statements can be found in the literature, and applications with more complex requirements may implement them, including for example the requirement of representing 'non-conformance' tested by specific procedures. The GeoDCAT Application Profile, for example, suggests a "provisional mapping" for extended profiles, which re-uses the PROV data model for provenance (see Annex II.14 at [GeoDCAT-AP]). Such patterns come however at the cost of having to publish and exchange representations that are much more elaborate. They will also have to be aligned with the result of another ongoing efforts on data validation and the reporting thereof, as currently discussed around SHACL, for example. We have thus decided to postpone addressing these requirements for now.
6.12Express the conformance of a dataset with a quality policy
DQV introduces the classdqv:QualityPolicy to express that a Dataset or Distribution follows a policy or agreement that is chiefly defined by data quality concerns. DQV does not provide a complete framework for expressing policies. The classdqv:QualityPolicy is rather meant as an anchor point, through which DQV implementers can relate properties and classes of policy-dedicated vocabularies, such as ODRL [ODRL], to the core elements that define quality of datasets and distributions.
The example below specifies that a data provider grants the permission to access a dataset and commits to serve the data with a certain quality, more concretely, 99% availability of a SPARQL endpoint (distribution) associated with the dataset. This is expressed in ODRL as an offer with a duty on the service provider that states a constraint defined using a DQV metric (:sparqlEndpointUptime), for which measurements have to be greater than a certain percentage (99). Theodrl:assigner is the issuer of the policy statement; it is also the assignee of the duty to deliver the distribution as the policy requires it. There is no explicitly mentioned recipient for the policy itself, since this examples is about a generic data access scenario. Note that instances ofdqv:QualityPolicy could be instances of the classodrl:Agreement, in which case an odrl:assignee is likely to appear for the policy.
:serviceProvider a odrl:Party .:myDataset a dcat:Dataset ; dcat:distribution :myDatasetSparqlDistribution ;:myDatasetSparqlDistribution a dcat:Distribution .:policy1 a odrl:Offer, dqv:QualityPolicy ; odrl:permission [ a odrl:Permission ; odrl:target :myDataset ; odrl:action odrl:read ; odrl:assigner :serviceProvider; odrl:duty [ a odrl:Duty; odrl:assignee :serviceProvider; odrl:target :myDatasetSparqlDistribution ; odrl:constraint [ a odrl:Constraint ; prov:wasDerivedFrom :sparqlEndpointUptime; odrl:percentage "99"^^xsd:double ; odrl:operator odrl:gteq ] ] ] .
Note
The expression of constraints in ODRL seems quite unfit with expressing general constraints on values in RDF graphs, as we would require here. However, ODRL can be easily extended, and is schedule to undergo refinement in the context of theW3CPermissions & Obligations Expression Working Group. In the future implementers should investigate whether a general constraint expression language like the coming SHACL [SHACL] provides a more appropriate mechanism to be used on top of ODRL permissions and duties.
6.13Express dataset precision and accuracy
The need for documenting data precision (also sometimes referred to as "resolution") is a common requirement, in particular, when dealing with spatial data. The following example shows how DQV can meet this requirement.
:myDataset a dcat:Dataset ; dqv:hasQualityMeasurement :myDatasetPrecision, :myDatasetAccuracy .:myDatasetPrecision a dqv:QualityMeasurement ; dqv:isMeasurementOf :spatialResolutionAsDistance ; dqv:value "1000"^^xsd:decimal ; sdmx-attribute:unitMeasure <http://www.wurvoc.org/vocabularies/om-1.8/metre> . :spatialResolutionAsDistance a dqv:Metric; skos:definition "Spatial resolution of a dataset expressed as distance"@en ; dqv:expectedDataType xsd:decimal ; dqv:inDimension dqv:precision .
Precision can be alternatively expressed without unit of measure specifying spatial resolution by means of an "equivalent scale" with a fraction (e.g., 1:1,000, 1:1,000,000)
:myDataset a dcat:Dataset; dqv:hasQualityMeasurement :myDatasetPrecisionES . :myDatasetPrecisionES a dqv:QualityMeasurement ; dqv:isMeasurementOf :spatialResolutionAsEquivalentScale ; dqv:value "0.000001"^^xsd:decimal . :spatialResolutionAsEquivalentScale a dqv:Metric; skos:definition "Spatial resolution of a dataset expressed as equivalent scale, by using a representative fraction (e.g., 1:1,000, 1:1,000,000)."@en ; dqv:expectedDataType xsd:decimal ; dqv:inDimension dqv:precision .
or specifying the angular distance.
:myDataset a dcat:Dataset; dqv:hasQualityMeasurement :myDatasetPrecisionAS . :myDatasetPrecisionAS a dqv:QualityMeasurement ; dqv:isMeasurementOf :spatialResolutionAsAngularDistance ; dqv:value "3.5"^^xsd:decimal ; sdmx-attribute:unitMeasure <http://www.wurvoc.org/vocabularies/om-1.8/degree> .:spatialResolutionAsAngularDistance a dqv:Metric; skos:definition "Spatial resolution of a dataset expressed as angular distance"@en ; dqv:expectedDataType xsd:decimal ; dqv:inDimension dqv:precision .
Note that the precision (or resolution) of a dataset is not equivalent to its accuracy. High precision values are not necessarily accurate. High precision values can even be pointless, as when one asserts that Magna Carta was signed at1215-06-15T00:00:00. Accuracy is nonetheless an important dimension of data quality. Data accuracy metrics and measurements can be represented with DQV, as in the following example:
:myDatasetAccuracy a dqv:QualityMeasurement ; dqv:isMeasurementOf :spatialAccuracy ; dqv:value "98.2"^^xsd:decimal sdmx-attribute:unitMeasure <http://www.wurvoc.org/vocabularies/om-1.8/Percentage> . :spatialAccuracy a dqv:Metric; skos:definition "Percentage of spatial elements that are found accurate according to methodology XYZ"@en ; dqv:expectedDataType xsd:decimal ; dqv:inDimension ldqd:semanticAccuracy .
7.Dimensions and metrics hints
This section gathers relevant quality dimensions and ideas for corresponding metrics, which might be eventually represented as instances ofdqv:Category,dqv:Dimension anddqv:Metric. The goal of the Data Quality Vocabulary is not to define a normative list of dimensions and metrics. There are already several reference classifications available, which are the result of a lot of community work. Unifying them here seems both hard and not desirable, as fundamental approaches to quality vary between domains or even applications. This section provides instead a set of examples, starting from use cases included in theUse Cases & Requirements document. In particular, we offer the quality dimension proposed in ISO 25012 [ISOIEC25012] and Zaveri et al. [ZaveriEtAl] as two starting points. Ultimately, implementers will need to choose themselves the approach that fits best their needs. They can extend on these starting points, creating their own refinements of categories and dimensions, and of course their own metrics. They can mix existing approaches — we show that the proposals from ISO and Zaveri et al. are not completely disjoint. Implementers can also adopt completely different classifications, if existing ones do not fit their specific application scenarios. They should however be aware that relying on existing classifications and metrics increases interoperability, i.e., the chance that human and machine agents can properly understand and exploit their quality assessments.
7.1Statistics
Statistics are not systematically conceived as quality measures. Yet, statistics on datasets can be seen as quality indicators relevant for the dimensions listed in the rest of this section. For instance, the percentages of empty string attributes and of URIs that do not point to any useful information (either directly in the published data or via HTTP content negotiation) can indicate quality issues for compliance or completeness. These indicators can be published as such or aggregated with other statistics into further refined quality assessments, using the pattern withprov:wasDerivedFrom presented in the"Vocabulary Overview" section.
The following table gives examples of statistics that can be computed on a dataset and interpreted as quality indicators for completeness and understandability, coming from theVoID extension created for theAether tool. We invite readers interested in statistics to investigate whether elements coming from Aether or similar efforts can match their needs.
| Observation | Suggested term |
|---|
| Number of distinct external resources linked to | http://ldf.fi/void-ext#distinctIRIReferenceObjects |
| Number of distinct external resources used (including schema terms) | http://ldf.fi/void-ext#distinctIRIReferences |
| Number of distinct literals | http://ldf.fi/void-ext#distinctLiterals |
| Number of languages used | http://ldf.fi/void-ext#languages |
Note
The AetherVoID extension represents statistics as direct statements that have a dataset as subject and an integer as object. This pattern, which can be expected to be rather common, is different from the pattern that DQV inherits from daQ. Guidance on how DQV can work with other quality statistics vocabulary shall be provided with future versions of the DQV documentation.
7.2Quality dimensions defined in ISO/IEC 25012
ISO/IEC 25012 provides an example of quality dimensions grouped in three categories that can be adopted to document the quality of datasets. These quality dimensions and categories are listed in the table below.
| Category | Dimension | Definition |
|---|
| Inherent Data Quality | Accuracy | The degree to which data has attributes that correctly represent the true value of the intended attribute of a concept or event in a specific context of use. |
|---|
| Completeness | The degree to which subject data associated with an entity has values for all expected attributes and related entity instances in a specific context of use. |
| Consistency | The degree to which data has attributes that are free from contradiction and are coherent with other data in a specific context of use. It can be either or both among data regarding one entity and across similar data for comparable entities. |
| Credibility | The degree to which data has attributes that are regarded as true and believable by users in a specific context of use. Credibility includes the concept of authenticity (the truthfulness of origins, attributions, commitments). |
| Currentness | The degree to which data has attributes that are of the right age in a specific context of use. |
| Inherent and System-Dependent Data Quality | Accessibility | The degree to which data can be accessed in a specific context of use, particularly by people who need supporting technology or special configuration because of some disability. |
|---|
| Compliance | The degree to which data has attributes that adhere to standards, conventions or regulations in force and similar rules relating to data quality in a specific context of use. |
| Confidentiality | The degree to which data has attributes that ensure that it is only accessible and interpretable by authorized users in a specific context of use. Confidentiality is an aspect of information security (together with availability, integrity) as defined in ISO/IEC 13335-1:2004. |
| Efficiency | The degree to which data has attributes that can be processed and provide the expected levels of performance by using the appropriate amounts and types of resources in a specific context of use. |
| Precision | The degree to which data has attributes that are exact or that provide discrimination in a specific context of use. |
| Traceability | The degree to which data has attributes that provide an audit trail of access to the data and of any changes made to the data in a specific context of use. |
| Understandability | The degree to which data has attributes that enable it to be read and interpreted by users, and are expressed in appropriate languages, symbols and units in a specific context of use. Some information about data understandability are provided by metadata. |
| System-Dependent Data Quality | Availability | The degree to which data has attributes that enable it to be retrieved by authorized users and/or applications in a specific context of use. |
|---|
| Portability | The degree to which data has attributes that enable it to be installed, replaced or moved from one system to another preserving the existing quality in a specific context of use. |
| Recoverability | The degree to which data has attributes that enable it to maintain and preserve a specified level of operations and quality, even in the event of failure, in a specific context of use. |
DQV can express the dimensions and categories listed in the table above. The following example includes only an exemplification of the ISO dimensions and categories, which could be authoritatively provided by ISO. Semantic relation defined in SKOS can be exploited to related categories and dimensions. For example, in the following,skos:broader has been exploited to defineiso:inherentSystemDependentDataQuality as a specialization ofiso:inherentDataQuality andiso:systemDependentDataQuality.
# definition of ISO categoriesiso:inherentDataQuality a dqv:Category ; skos:prefLabel "Inherent Data Quality"@en. iso:systemDependentDataQuality a dqv:Category ; skos:prefLabel "System-Dependent Data Quality"@en. iso:inherentSystemDependentDataQuality a dqv:Category ; skos:prefLabel "Inherent and System-Dependent Data Quality"@en. skos:broader iso:inherentDataQuality, iso:systemDependentDataQuality .# definition of ISO dimensionsiso:accuracy a dqv:Dimension ; dqv:inCategory iso:inherentDataQuality ; skos:prefLabel "Accuracy"@en; skos:definition "The degree to which data has attributes that correctly represent the true value of the intended attribute of a concept or event in a specific context of use."@en .iso:completeness a dqv:Dimension ; dqv:inCategory iso:inherentDataQuality ; skos:prefLabel "Completeness"@en; skos:definition "The degree to which subject data associated with an entity has values for all expected attributes and related entity instances in a specific context of use."@en .iso:consistency a dqv:Dimension ; dqv:inCategory iso:inherentDataQuality ; skos:prefLabel "Consistency"@en; skos:definition "The degree to which data has attributes that are free from contradiction and are coherent with other data in a specific context of use. It can be either or both among data regarding one entity and across similar data for comparable entities."@en .# ... ...iso:accessibility a dqv:Dimension ; dqv:inCategory iso:inherentSystemDependentDataQuality ; skos:prefLabel "Accessibility"@en; skos:definition "The degree to which data can be accessed in a specific context of use, particularly by people who need supporting technology or special configuration because of some disability."@en .# ... etc ...
7.3Quality dimensions defined for Linked Data
Zaveri et al. provides a review of quality dimensions, which is specifically suited for Linked Data [ZaveriEtAl].
| Category | Dimension | Definition |
|---|
| Accessibility dimensions | Availability | Availability of a dataset is the extent to which data (or some portion of it) is present, obtainable and ready for use. |
|---|
| Licensing | Licensing is defined as the granting of permission for a consumer to re-use a dataset under defined conditions. |
| Interlinking | Interlinking refers to the degree to which entities that represent the same concept are linked to each other, be it within or between two or more data sources. |
| Security | Security is the extent to which data is protected against alteration and misuse. |
| Performance | Performance refers to the efficiency of a system that binds to a large dataset, that is, the more performant a data source is the more efficiently a system can process data. |
| Intrinsic dimensions | Syntactic validity | Syntactic validity is defined as the degree to which an RDF document conforms to the specification of the serialization format. |
|---|
| Semantic accuracy | Semantic accuracy is defined as the degree to which data values correctly represent the real world facts. |
| Consistency | Consistency means that a knowledge base is free of (logical/formal) contradictions with respect to particular knowledge representation and inference mechanisms. |
| Conciseness | Conciseness refers to the minimization of redundancy of entities at the schema and the data level. |
| Completeness | Completeness refers to the degree to which all required information is present in a particular dataset. |
| Contextual dimensions | Relevancy | Relevancy refers to the provision of information which is in accordance with the task at hand and important to the users’ query. |
|---|
| Trustworthiness | Trustworthiness is defined as the degree to which the information is accepted to be correct, true, real and credible. |
| Understandability | Understandability refers to the ease with which data can be comprehended without ambiguity and be used by a human information consumer. |
| Timeliness | Timeliness measures how up-to-date data is relative to a specific task. |
| Representational dimensions | Representational-conciseness | Representational-conciseness refers to the representation of the data, which is compact and well formatted. |
|---|
| Interoperability | Interoperability is the degree to which the format and structure of the information conforms to previously returned information as well as data from other sources. |
| Interpretability | Interpretability refers to technical aspects of the data, that is, whether information is represented using an appropriate notation and whether the machine is able to process the data. |
| Versatility | Versatility refers to the availability of the data in different representations and in an internationalized way. |
DQV can express these dimensions and categories as shown in the following example. The encoding of all the dimensions and categories mentioned above can be found athttps://www.w3.org/2016/05/ldqd.
# definition of categories from Zaveri et alldqd:accessibilityDimensions a dqv:Category ; skos:prefLabel "Accessibility"@en. ldqd:intrinsicDimensions a dqv:Category ; skos:prefLabel "Intrinsic dimensions"@en. ldqd:contextualDimensions a dqv:Category ; skos:prefLabel "Contextual dimensions"@en. ldqd:representationalDimensions a dqv:Category ; skos:prefLabel "Representational Dimensions"@en. #definition of dimensions from Zaveri et alldqd:availability a dqv:Dimension ; dqv:inCategory ldqd:accessibilityDimensions ; skos:prefLabel "Availability"@en; skos:definition "Availability of a dataset is the extent to which data (or some portion of it) is present, obtainable and ready for use."@en .ldqd:licensing a dqv:Dimension ; dqv:inCategory ldqd:accessibilityDimensions ; skos:prefLabel "Licensing"@en; skos:definition "Licensing is defined as the granting of permission for a consumer to re-use a dataset under defined conditions."@en .ldqd:interlinking a dqv:Dimension ; dqv:inCategory ldqd:accessibilityDimensions ; skos:prefLabel "Consistency"@en; skos:definition "Interlinking refers to the degree to which entities that represent the same concept are linked to each other, be it within or between two or more data sources."@en .# ... etc ...
7.3.1Expressing relations between quality dimensions
In Zaveri Et Al. [ZaveriEtAl] some dimensions are not completely independent and may be related. These relationships can be represented in DQV by using the appropriate SKOS properties or by specializing the SKOS properties if more specific semantics must be expressed. For example,ldqd:availability is related toldqd:performance andldqd:interlinking, whilstldqd:semanticAccuracy is related toldqd:timeliness,ldqd:trustworthiness,ldqd:consistency,ldqd:syntaticValidity andldqd:completeness.
ldqd:availability skos:related ldqd:performance , ldqd:interlinking .ldqd:semanticAccuracy skos:related ldqd:timeliness , ldqd:trustworthiness , ldqd:consistency , ldqd:syntaticValidity , ldqd:completeness , ldqd:interlinking . ldqd:consistency skos:related ldqd:conciseness , ldqd:syntaticValidity , ldqd:interoperability .ldqd:interoperability skos:related ldqd:conciseness , ldqd:syntaticValidity .ldqd:conciseness skos:related ldqd:completeness , ldqd:representationalConciseness .ldqd:interpretability skos:related ldqd:versatility .# Note: skos:related is a symmetric property, hence from every statement# ex:subject skos:related ex:object in this example, one can infer that # the statement ex:object skos:related ex:subject is true.
Dimensions can also be related across different categorizations. For example, in the following, we present two possible links between dimensions from ISO/IEC 25012 [ISOIEC25012] and Zaveri et al. Here we assume that completeness is equivalent across both classifications and that ISO's credibility is one specific facet of trustworthiness in Zaveri et al. (see Definition 12 in [ZaveriEtAl]). We pencil more such possible relationships in Annex C.
ldqd:completeness skos:exactMatch iso:completeness .ldqd:trustworthiness skos:narrowMatch iso:credibility .
7.4Examples of metrics
This section presents examples of metrics inspired by those reviewed in Zaveri et al. [ZaveriEtAl], in order to further illustrate how dqv:Metric can be instantiated. Note that they are not all specific to Linked Data quality, as some dimensions in Zaveri et al. matches the dimensions of ISO/IEC 25012 (see previous sub-section and Annex).
Note
These examples are just some of the possible ones. They show metrics for different dimensions and kinds of dataset distributions. We might consider reorganizing examples around specific criteria (e.g., include at least a metric for each dimension, or focus on metrics for a specific kind of distribution, e.g., RDF, JSON, CSV). We might also consider to add further examples about derived metrics, multivalued metrics and extra parameters, once we have solved the remaining issues.
:downloadURLAvailabilityMetric a dqv:Metric ; skos:definition "It checks if dcat:downloadURL is available and if its value is dereferenceable."@en ; dqv:inDimension ldqd:availability ; dqv:expectedDataType xsd:boolean .:sparqlAvailabilityMetric a dqv:Metric ; skos:definition "It checks if a void:sparqlEndpoint is specified for a dataset and if the server responds to a SPARQL query."@en ; dqv:inDimension ldqd:availability ; dqv:expectedDataType xsd:boolean .:misreportedContentTypeMetric a dqv:Metric ; skos:definition "It detects whether the HTTP response contains the header field stating the appropriate content type of the returned file, e.g. application/rdf+xml"@en ; dqv:inDimension ldqd:availability ; dqv:expectedDataType xsd:boolean .:licensingMetric a dqv:Metric ; skos:definition "It detects the indication of a license in a the DCAT/VoID description or in the dataset of a license itself."@en ; dqv:inDimension ldqd:licensing ; dqv:expectedDataType xsd:boolean .:highThroughput a dqv:Metric ; skos:definition "It represents the maximum number of answered HTTP-requests per second."@en ; dqv:inDimension ldqd:performance ; dqv:expectedDataType xsd:integer .:sparqlScalability a dqv:Metric ; skos:definition "It detects whether the time to answer an amount of ten requests divided by ten is not longer than the time it takes to answer one request."@en ; dqv:inDimension ldqd:performance ; dqv:expectedDataType xsd:boolean .:noRDFSyntaxError a dqv:Metric ; skos:definition "It returns the number of syntax errors detected by an RDF validator."@en ; dqv:inDimension ldqd:syntacticValidity; dqv:expectedDataType xsd:integer .:noJSONSyntaxError a dqv:Metric ; skos:definition "It returns the number of syntax errors detected by an JSON validator."@en ; dqv:inDimension ldqd:syntacticValidity; dqv:expectedDataType xsd:integer .:populationCompletenessMetric a dqv:Metric ; skos:definition "Ratio between the number of objects represented in the dataset and the number of objects expected to be represented according to the declared dataset scope."@en ; dqv:inDimension ldqd:completeness ; dqv:expectedDataType xsd:double .
D.Defining and using parameters for metrics
Some metrics come with mandatory or optionalparameters. Input parameters in particular play a key role in describing metrics and measurements: their values specify how a metric is applied to obtain a measurement. E.g., when a quality metric is defined as a general procedure that can be applied to different types of statement in the dataset, a parameter can be used to indicate the type a given measurement has focused on. Or when various components that contribute to a metric's computation can be assigned different weights.Section 6.10 presents examples of metrics with such parameters (:onLanguage and:onProperty).
During the design of DQV, questions were raised about the modeling of parameters in RDF and similar frameworks, especially about expressing that some parameters can be mandatory for measurements of some metrics (seeIssue-223). The Working Group has postponed the issue, as we felt it was a much less mature aspect of the state of the art, and the we lacked resource to articulate a consensual decision. This appendix presents some of the options we have considered. We welcome readers' feedback!
D.1Modeling parameters
The first issue is the "meta-modeling" of parameters, especially, how properties such as:onLanguage should be represented using the modeling constructs of RDF(S) and OWL [OWL-primer]. One can mint a new property:parameterValue:
:onLanguage rdfs:subPropertyOf :parameterValue .
Alternatively, one can create a class of (parameter) properties:ParameterProperty having properties like:onLanguage as instances:
:onLanguage a :ParameterProperty .
Both patterns allow to represent parameters for quality metrics. They have their own strengths and weaknesses in terms of modeling elegance, complexity, etc. However, we could not carry a full analysis and recommend a preferred approach. Moreover, at the time of publishing it is unclear whether using either of them delivers crucial value to implementers interested in the basic needs presented inSection 6.10. Especially, solutions for representing constraints using OWL axioms or Data Cube's Data Structure Definition, as discussed in the next sub-section, do not seem to be seriously impacted by choosing either of these two meta-modeling approaches, or none.
Note finally that one may opt for a more basic solution that avoids explicit modeling parameters altogether: hardcoding parameters in the definition of metrics. Namely, in order to represent the measurement alternatives specific to languages (say, English or Italian) and types of property (say,skos:prefLabel orskos:altLabel), one could "split" original metrics like:importingForPropertyPercentage into new metrics that correspond to the relevant combination of parameters, as in the following:
:importingForPropertyPercentagePrefLabelIt a dqv:Metric ; skos:definition "Ratio between novel Italian preferred labels gained via skos:exactMatch links and Italian preferred labels already in the datasets."@en dqv:expectedDataType xsd:double ; dqv:inDimension :completeness . :importingForPropertyPercentagePrefLabelEn a dqv:Metric ; skos:definition "Ratio between novel English preferred labels gained via skos:exactMatch links and English preferred labels already in the datasets."@en dqv:expectedDataType xsd:double ; dqv:inDimension :completeness . :importingForPropertyPercentageAltLabelIt a dqv:Metric ; skos:definition "Ratio between novel Italian alternative labels gained via skos:exactMatch links and Italian alternative labels already in the datasets."@en dqv:expectedDataType xsd:double ; dqv:inDimension :completeness . :importingForPropertyPercentageAltLabelEn a dqv:Metric ; skos:definition "Ratio between novel English alternative labels gained via skos:exactMatch links and English alternative labels already in the datasets."@en dqv:expectedDataType xsd:double ; dqv:inDimension :completeness .
This approach can be useful, as seen at the end of theAppendix on RDF Data Cube. It is however much less easy to implement, should the number of parameters or the sets of their possible values grow high, or, even worse, when the sets of possible values are not known at data modeling stage.
D.2Parameter constraints
The second issue is the expression of constraints on parameter usage. For example, specifying that every measurement of the metric:importingForPropertyPercentage should be the subject of an:onLanguage statement that indicates the language considered for the measurement.
One solution is to employ the modeling features of the Web Ontology Language OWL and create a new (sub-)class ofdqv:Metric that requires the presence of the parameter for its instances, as in this example based on the case fromSection 6.10:
:MetricWithLanguageParameter rdfs:subClassOf dqv:Metric, [ a owl:Restriction ; owl:onProperty :onLanguage ; owl:cardinality "1"^^xsd:nonNegativeInteger ] .:linksetImportingENSKOSPrefLabel a :MetricWithLanguageParameter ; skos:definition "Importing on English prefLabel"@en; :onLanguage "en" .
This solution can be used for 'meta-parameters', that is, when one considers a class of parameter-dependent metrics — where assigning a value for a parameter allows one to define an individual metric in the class. However, this case is different from the one of the metric "multilingual importing" inSection 6.10. This metric indeed specifies that its measurements should be bound to specific languages. But it does not say which: the parameter value needs to be assigned at the level of the actual measurements. Representing that would require defining a new class of measurements, say,:MeasurementWithLanguageParameter, as follows:
# The following statements should be added to the ones from Section 6.10:MeasurementWithLanguageParameter rdfs:subClassOf dqv:Measurement, [ a owl:Restriction ; owl:onProperty :onLanguage ; owl:cardinality "1"^^xsd:nonNegativeInteger ] .:MetricWithLanguageParameter rdfs:subClassOf dqv:Metric, [ a owl:Restriction ; owl:onProperty [ owl:inverseOf dqv:isMeasurementOf ] ; owl:allValuesFrom :MeasurementWithLanguageParameter ] .:importingForPropertyPercentage a :MetricWithLanguageParameter .:measurement_exactMatchAltLabelItDataset1 dqv:isMeasurementOf :importingForPropertyPercentage .
Implementers should note that this pattern is impacted by OWL'sopen-world assumption. The class definitions above do not lead to constraints in the "traditional" understanding! I.e., if an OWL reasoner finds a measurement of:importingForPropertyPercentage without an:onProperty statement, it will not raise a formal inconsistency error. It will instead just assume that the statement must have been asserted in another RDF graph elsewhere. Other approaches may be followed to palliate this. The RDF Data Cube vocabulary offers a Data Structure Definition mechanism, which we exemplify in theAppendix on RDF Data Cube. This solution allows a Data Cube validation tool to flag a language-less measurement of:importingForPropertyPercentage as incomplete. However, as hinted in this Appendix, Data Cube's Data Structures are harder to apply when quality assessments of different types are mixed together. A general constraint expression language like the coming SHACL [SHACL] may provide an appropriate solution. However, SHACL is still under development at the time of writing this document.
E.Compatibility with RDF Data Cube
The RDF Data Cube vocabulary [Vocab-Data-Cube] provides a means to represent multi-dimension data, including statistics. Measurements represented in DQV can fit this approach, and there might be many benefits in representing them in a way compatible with Data cube prescriptions — starting with the possibility to load measurement data in visualization or processing tools compatible with Data Cube.
In DQV, instances of dqv:QualityMeasurement are also instances of Data Cube's qb:Observation. A dataset (RDF graph) of DQV measurements can be made further compatible with Data Cube by specifying an appropriate (Data Cube) Data Structure Definition, following a pattern introduced by the daQ vocabulary [DaQ-RDFCUBE]. The following example is aimed at representing measurements for the metrics capturing thequality of a linkset presented in Section 6.10:
:linksetQualityMeasurements a dqv:QualityMeasurementDataset ; qb:structure ex:dsd . ex:dsd a qb:DataStructureDefinition ; ## Expressing Data Cube dimensions qb:component [ qb:dimension dqv:isMeasurementOf ; qb:order 1 ] ; qb:component [ qb:dimension dqv:computedOn ; qb:order 2 ] ; qb:component [ qb:dimension dcterms:date ; qb:order 3 ] ; ## Expressing the Data Cube measure qb:component [ qb:measure dqv:value ; ] ; ## Expressing the Data Cube attribute (here, unit of measurement) qb:component [ qb:attribute sdmx-attribute:unitMeasure ; qb:componentRequired false ; qb:componentAttachment qb:DataSet ] .
Allowing data publishers to use the propertiesdqv:isMeasurementOf anddqv:computedOn in such data structures is actually the reason why they have been defined as instances ofqb:DimensionProperty>. The following statements represent the linking of actual measurements in Section 6.10 to the dataset specified by the Data Structure Definition:
:measurement_exactMatchAltLabelItDataset1 qb:dataSet :linksetQualityMeasurements .:measurement_exactMatchAltLabelItDataset2 qb:dataSet :linksetQualityMeasurements .:measurement_exactMatchAltLabelEnDataset1 qb:dataSet :linksetQualityMeasurements .:measurement_exactMatchAltLabelEnDataset2 qb:dataSet :linksetQualityMeasurements .:measurement_exactMatchPrefLabelItDataset1 qb:dataSet :linksetQualityMeasurements .:measurement_exactMatchprefLabelItDataset2 qb:dataSet :linksetQualityMeasurements .
DQV users should be aware that applying Data Cube Data Structure Definitions to their quality information datasets has a broad impact on the possible content of these. All resources that are said to be in the dataset (using theqb:dataSet property) are indeed expected to have the components defined as mandatory in the data structure!
RDF Data Cube also states that "no two qb:Observations in the same qb:DataSet may have the same value for all dimensions". This integrity constraint implies that it is not allowed to have two distinct measurements for the same metric, resource and date. Metrics depending on parameters such as the ones fromSection 6.10 shall be used with extra care so as to adhere to this constraint: data publishers will probably need to represent quality measurements for the same resource and date, but which are obtained by applying distinct parameters.
Adopters of DQV and Data Cube can consider two modelling options for addressing this:
- To extend the above Data Structure with the desired parameters;
- To define a new Metric for each combination of the parameters values.
Let's apply the first option to the example ofSection 6.10. The “Multilingual importing” metric there requires to manage two parameters,:onProperty and:onLanguage, which gave raise to one instance ofowl:DataProperty each. These parameters can be also expressed as instancesqb:DimensionProperty and the above Data Structure extended as follows:
## Adding a new type to the parameter properties :onLanguage a qb:DimensionProperty . :onProperty a qb:DimensionProperty . ## Extending the structure of daq:dsq with two new dimensions ex:dsd qb:component [ qb:dimension :onProperty ; qb:order 4 ] ; qb:component [ qb:dimension :onLanguage ; qb:order 5 ] .
Alternatively, data publishers can keep to the original Data Structure. But in this case they should be prepared to not use the parameter properties introduced in 6.10. Instead, they could use the "split" metrics presented at the end of theAppendix on parameters. This solution is however not always applicable, as this Appendix shows it.
