This
document
provides
a
framework
in
which
the
quality
of
a
dataset
can
be
described,
whether
by
the
dataset
publisher
or
by
a
broader
community
of
users.
It
does
not
provide
a
formal,
complete
definition
of
quality,
rather,
it
sets
out
a
consistent
means
by
which
information
can
be
provided
such
that
a
potential
user
of
a
dataset
can
make
his/her
own
judgment
about
its
fitness
for
purpose.
Status
of
This
Document
This
section
describes
the
status
of
this
document
at
the
time
of
its
publication.
Other
documents
may
supersede
this
document.
A
list
of
current
W3C
publications
and
the
latest
revision
of
this
technical
report
can
be
found
in
the
W3C
technical
reports
index
at
http://www.w3.org/TR/.
This
The
model
for
the
Data
Quality
Vocabulary
is
an
early
draft
of
nearing
maturity,
but
the
DQV.
Its
publication
Working
Group
is
designed
to
provoke
comment
seeking
feedback
on
the
overall
direction
foreseen
as
much
as
the
a
number
of
specific
details.
issues
highlighted
in
the
document
below.
Publication
as
a
First
Public
Working
Draft
does
not
imply
endorsement
by
the
W3C
Membership.
This
is
a
draft
document
and
may
be
updated,
replaced
or
obsoleted
by
other
documents
at
any
time.
It
is
inappropriate
to
cite
this
document
as
other
than
work
in
progress.
This
document
was
produced
by
a
group
operating
under
the
5
February
2004
W3C
Patent
Policy
.
The
group
does
not
expect
this
document
to
become
a
W3C
Recommendation.
W3C
maintains
a
public
list
of
any
patent
disclosures
made
in
connection
with
the
deliverables
of
the
group;
that
page
also
includes
instructions
for
disclosing
a
patent.
An
individual
who
has
actual
knowledge
of
a
patent
which
the
individual
believes
contains
Essential
Claim(s)
must
disclose
the
information
in
accordance
with
section
6
of
the
W3C
Patent
Policy
.
The
Data
on
the
Web
Best
Practices
Working
Draft
points
has
pointed
out
the
relevance
of
publishing
information
about
the
quality
of
data
published
on
the
Web
.
Accordingly,
the
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
DCAT
[
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.
Accordingly,
In
accordance,
we
attach
a
lot
of
importance
to
allowing
many
actors
to
assess
the
quality
of
datasets
and
publish
their
annotations,
certificates
and
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
and
aggregators,
data
consumers
can
make
relevant
quality
assessments
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,
constructs
like
quality
measures,
the
Data
Quality
Vocabulary
puts
a
lot
of
emphasis
on
feedback,
annotation,
agreements
and
the
provenance
of
the
metadata.
metadata
that
describes
them.
2.
Conformance
As
well
as
sections
marked
as
non-normative,
all
authoring
guidelines,
diagrams,
examples,
and
notes
in
this
specification
are
non-normative.
Everything
else
in
this
specification
is
normative.
3.
Namespaces
The
namespace
for
DQV
is
provisionally
set
as
http://www.w3.org/ns/dqv#
.
DQV,
however,
seeks
to
re-use
elements
from
other
vocabularies,
following
the
best
practices
for
data
vocabularies
identified
by
the
Data
on
the
Web
Best
Practices
Working
Group.
Issue
1
Note
The
Working
Group
is
considering
putting
all
new
classes
and
properties
(together
with
defined
in
the
ones
of
DWBP
Vocabularies
in
the
DCAT
namespace.
As
an
attempt
to
stimulate
reactions
which
might
help
in
taking
a
decision,
the
Dataset
Usage
Vocabulary
)
in
will
be
moved
under
the
DCAT
namespace
.
(
Issue-179
).
namespace.
In
case
of
positive
reactions
to
the
DUV
choice,
the
data
quality
vocabulary
might
consider
to
go
in
the
same
direction.
The
table
below
indicates
the
full
list
of
namespaces
and
prefixes
used
in
this
document.
Prefix
Namespace
daq
http://purl.org/eis/vocab/daq#
dcat
http://www.w3.org/ns/dcat#
dcterms
http://purl.org/dc/terms/
dqv
http://www.w3.org/ns/dqv#
duv
http://www.w3.org/ns/duv#
oa
http://www.w3.org/ns/oa#
prov
http://www.w3.org/ns/prov#
Issue
2
Are
we
actually
allowed
by
W3C
to
re-use
elements
from
DAQ
?
This
vocabulary
is
not
a
community
standard
and
its
guarantee
of
sustainability
may
be
judged
not
good
enough.
A
possible
way
forward
would
be
to
declare
all
relevant
classes
in
the
DQV
namespace
but
then
declare
them
all
as
owl:equivalentClass/property
with
their
DAQ
counterparts.
(
Issue-180
).
4.
3.
Vocabulary
Overview
The
following
vocabulary
is
based
on
DCAT
[
vocab-dcat
]
that
it
extends
with
a
number
of
additional
properties
and
classes
suitable
for
expressing
the
quality
of
a
dataset.
The
quality
of
a
given
dataset
or
of
distribution
is
assessed
via
a
number
of
observed
properties.
For
instance,
one
may
consider
a
dataset
to
be
of
high
quality
because
it
complies
with
to
a
specific
standard
while
for
other
use
cases
use-cases
the
quality
of
the
data
will
depend
on
its
level
of
interlinking
with
other
datasets.
To
express
these
properties
an
instance
of
a
dcat:Dataset
or
dcat:Distribution
can
be
related
to
four
different
classes:
dqv:QualityAnnotation
represents
feedback
feedbacks
and
quality
certificates
given
about
the
dataset
or
a
distribution
of
it.
its
distribution.
dcterms:Standard
represents
a
standard
the
dataset
or
its
distribution
conforms
to.
prov:Entity
represents
an
entity
involved
in
the
provenance
of
the
dataset
or
distribution.
dqv:QualityMeasure
represents
a
metric
value
providing
quantitative
or
qualitative
information
about
the
dataset
or
distribution.
Note
Textual
description
of
the
diagram
will
be
added.
Fig.
1
Data
model
showing
the
main
relevant
classes
and
their
relations.
N.B.
N.B.:
"graph
containment"
refers
to
the
inclusion
of
quality
statements
in
(RDF)
graphs,
e.g.
for
capturing
the
provenance
of
quality
statements
(see
later
example
).
Issue
3
What
is
the
relation
between
duv:Feedback
and
dqv:UserFeedback
?
(
Issue-165
).
)
Issue
4
1
Should
we
have
only
the
existing
class
daq:QualityGraph
dqv:QualityMeasureDataset
or
keep
the
define
a
new
class
dqv:QualityMetadata
to
represent
a
set
of
statements
providing
quantitative
and/or
qualitative
information
about
the
dataset
or
distribution.
One
could
be
a
sub-class
of
the
other.
(
Issue-181
)
Issue
5
2
The
label
Is
dqv:QualityPolicy
a
subclass
of
daq:QualityGraph
does
not
fit
well
with
dcterms:Standard
?
The
wording
in
the
current
model.
DAQ
graphs
are
meant
to
contain
measures.
In
our
context
a
"quality
graph"
has
Dublin
Core
specification
is
very
open
("A
basis
for
comparison;
a
wider
scope:
actually
reference
point
against
which
other
things
can
be
evaluated"),
but
the
role
of
representing
overall
quality
graphs
label
is
currently
played
by
dqv:QualityMetadata
.
(
Issue-182
)
Issue
6
We
may
want
to
consider
a
revision
of
DCAT
to
make
dcat:Dataset
and
dcat:Distribution
subclasses
quite
restrictive.
At
the
time
of
prov:Entity
.
(
Issue-183
discussion
)
Issue
7
Is
a
dqv:ServiceLevelAgreement
a
kind
majority
of
certificate,
or
a
standard?
(
Issue-184
)
Issue
8
dqv:QualityAnnotation
WG
members
is
foreseen
as
a
subclass
of
oa:Annotation
.
The
instances
of
this
class
should
have
a
oa:motivatedBy
statement
ok
with
subclassing,
but
we
welcome
public
feedback
before
making
a
an
instance
of
oa:Motivation
,
which
reflects
a
quality
assessment
purpose.
We
plan
to
define
is
as
dqv:qualityAssessment
.
final
decision
(
Issue-185
Issue-199
)
5.
4.
Vocabulary
specification
Note
This
section
is
work
in
progress.
We
will
include
later
more
tables
with
specification
of
individual
classes
and
properties.
5.1
4.1
Class:
Quality
Measure
DQV
defines
quality
measures
as
specific
instances
of
DAQ
DQV
observations,
adapting
the
DAQ
quality
metrics
framework
:
[
DaQ
],
[
DaQ-RDFCUBE
]:
A
Quality
Dimension
is
a
characteristic
of
a
dataset
relevant
to
the
consumer
(e.g.,
the
availability
of
a
dataset).
A
Quality
Metric
is
gives
a
procedure
for
measuring
a
data
quality
dimension,
which
is
abstract,
by
observing
a
concrete
quality
indicator.
There
are
usually
multiple
metrics
per
dimension;
e.g.,
availability
can
be
indicated
by
the
accessibility
of
a
SPARQL
endpoint,
or
of
an
RDF
dump.
The
value
of
a
metric
can
be
numeric
(e.g.,
for
the
metric
“human-readable
labeling
of
classes,
properties
and
entities”,
the
percentage
of
entities
having
an
rdfs:label
or
rdfs:comment)
or
boolean
(e.g.
whether
or
not
a
SPARQL
endpoint
is
accessible).
For
example,
a
dimension
could
be
"multilinguality"
and
two
metrics
could
be
"ratio
of
literals
with
language
tags"
and
"number
of
different
language
tags".
The
following
property
properties
should
be
used
on
this
class:
daq:metric
.
The
following
property
may
be
used
for
this
class:
dqv:hasMetric
,
dqv:value
,
qb:dataSet
.
Issue
3
Should
(and
if
yes,
how)
DQV
represent
multiple/derived
values
for
a
metric
(e.g.,
average
or
normalized
value)?
(
Issue-222
)
Issue
4
Should
(and
if
yes,
how)
DQV
represent
parameters
for
a
metric
applied
for
computing
a
specific
quality
measure
(e.g.,a
specific
setting
of
weights)?
(
Issue-223
)
There
might
Is
dqv:hasMetric
correct
or
should
it
be
no
need
for
a
subclass
link
dqv:metric?
The
issue
is
complicated
by
the
relationship
between
dqv:QualityMeasure
DQV
and
daq:Observation
.
I.e.,
we
could
re-use
daq:Observation
directly.
(
Issue-186
both
QB
and
daQ.
Issue-231
)
Refers
to
the
resource
(e.g.,
a
dataset,
a
linkset,
a
graph,
a
set
of
triples)
on
which
the
quality
measurement
is
performed.
In
the
DQV
context,
this
property
is
generally
expected
to
be
used
in
statements
in
which
objects
are
instances
of
dcat:Dataset
and
dcat:Distribution
.
In
daQ,
the
property
daq:expectedDataType
associates
each
metric
to
the
expected
data
type
for
its
observed
value.
Data
types
for
observed
values
are
restricted
to
xsd:anySimpleType
(e.g.
xsd:boolean,
xsd:double
etc…).
Is
the
current
practice
of
using
daq:expectedDataType
in
daQ
appropriate?
Isn't
the
restriction
to
xsd:anySimpleType
too
narrow?
(
Issue-224
)
Dimensions
are
meant
to
systematically
organize
metrics.
The
Data
Quality
Vocabulary
defines
no
specific
cardinality
constraints
for
dqv:hasDimension,
since
distinct
quality
dimension
is
frameworks
might
have
different
perspectives
over
a
metric.
A
metric
belongs
may
therefore
be
associated
to
exactly
more
than
one
dimension.
However,
those
who
define
new
quality
measures
should
try
to
avoid
this
as
much
as
possible
and
assign
only
one
dimension
to
the
metrics
they
define.
Issue
10
4.2
Class:
Dimension
Do
we
want
to
keep
the
same
occurrence
constraints
as
defined
in
DAQ
(for
example,
that
every
metric
The
following
properties
should
belong
to
exactly
one
dimension)?
In
this
specific
case
this
may
be
demanding
too
much
used
on
this
class:
dqv:hasCategory
.
Represents
criteria
relevant
for
assessing
quality.
Each
quality
data
publishers:
it
could
be
that
a
metric
does
not
clearly
belong
to
a
dimension,
dimension
must
have
one
or
that
a
more
metric
to
measure
it.
A
dimension
is
in
scope
for
several
dimensions.
(
Issue-187
linked
with
a
category
using
the
dqv:hasDimension
property.
1
Categories
are
meant
to
systematically
organize
dimensions.
The
Data
Quality
Vocabulary
defines
no
specific
cardinality
constraints
for
dqv:hasCategory,
since
distinct
quality
frameworks
might
have
different
perspectives
over
a
dimension.
A
dimension
may
therefore
be
associated
to
more
than
one
category.
However,
those
who
define
new
quality
measures
should
try
to
avoid
this
as
much
as
possible
and
assign
only
one
category
to
the
dimensions
they
define.
Represents
quality
annotations,
including
rating,
quality
certificate,
feedback
that
can
be
associated
to
datasets
or
distributions.
Quality
annotations
must
have
one
oa:motivatedBy
statement
with
an
instance
of
oa:Motivation
(and
skos:Concept),
which
reflects
a
quality
assessment
purpose.
We
define
this
instance
as
dqv:qualityAssessment.
Should
we
exploit
predefined
instances
of
oa:Motivation
to
further
characterize
a
user's
feedback
purposes?
(
Issue-201
)
Combining
the
predefined
instances
of
oa:Motivation
with
the
dqv:qualityAssessment
we
could
distinguish
different
kinds
of
for
user
feedbacks,
for
example:
dqv:qualityAssessment
plus
oa:editing
might
indicate
a
request
for
a
modification
or
edit,
which
relates
to
the
quality
of
the
target
dataset/distribution
dqv:qualityAssessment
plus
oa:questioning
might
express
a
question
issued
about
specific
quality
of
the
dataset/distribution
dqv:qualityAssessment
plus
oa:classification
might
represent
the
assignment
of
a
classification
type,
typically
from
a
controlled
vocabulary
or
list,
to
the
target
resource(s).
For
example,
it
could
be
used
to
classify
a
dataset/distribution
against
a
rating
system
(e.g.,
the
5
Stars
linked
open
data
rating
system).
Refers
to
the
performed
quality
measurements.
Quality
measurements
can
be
performed
to
any
kind
of
resource
(e.g.,
a
dataset,
a
linkset,
a
graph,
a
set
of
triples).
However,
in
the
DQV
context,
this
property
is
generally
expected
to
be
used
in
statements
in
which
subjects
are
instances
of
dcat:Dataset
and
dcat:Distribution
.
This
section
shows
some
examples
to
illustrate
the
application
of
the
Dataset
Quality
Vocabulary.
Note
This
section
is
still
work
in
progress.
Further
examples
will
be
provided
as
soon
as
some
of
the
pending
issues
are
resolved.
We
invite
the
public
to
contact
the
editors
and
submit
relevant
examples
of
quality
data,
even
not
yet
represented
in
DQV.
We
welcome
your
input!
NB:
in
the
remainder
of
this
section,
the
prefix
"
:
"
refers
to
http://example.org/
6.1
5.1
Express
a
quality
assessment
with
quality
metrics
Let
us
consider
a
dataset
myDataset
,
and
its
distribution
myDatasetDistribution
,
:myDataset
a dcat:Dataset ;
dct:title "My dataset" ;
dcat:distribution :myDatasetDistribution
.
:myDatasetDistribution
a dcat:Distribution ;
dcat:downloadURL <http://www.example.org/files/mydataset.csv> ;
dct:title "CSV distribution of dataset" ;
dcat:mediaType "text/csv" ;
dcat:byteSize "87120"^^xsd:decimal
.
: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
measures
for
myDatasetDistribution
.
:myDatasetDistribution
dqv:hasQualityMeasure :measure1, :measure2
.
:measure1
a dqv:QualityMeasure ;
dqv:computedOn :myDatasetDistribution ;
dqv:hasMetric :csvAvailabilityMetric ;
dqv:value "1.0"^^xsd:double
.
:measure2
a dqv:QualityMeasure ;
dqv:computedOn :myDatasetDistribution ;
dqv:hasMetric :csvConsistencyMetric ;
dqv:value "0.5"^^xsd:double
.
#definition of dimensions and metrics
:availabity
a dqv:Dimension ;
dqv:hasCategory :category1;
.
:consistency
a dqv:Dimension ;
dqv:hasCategory :category2
.
:csvAvailabilityMetric
a dqv:Metric ;
dqv:hasDimension :availabity
.
:csvConsistencyMetric
a dqv:Metric ;
dqv:hasDimension :consistency
.
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.
:measure1
a dqv:QualityMeasure ;
# when daq:computedOn ranges into a dcat:Distribution/dcat:Dataset,
# dqv:hasQualityMeasure is likely to be the inverse of daq:computedOn ?
# In any case we could remove this statement.
daq:computedOn :myDatasetDistribution ;
daq:metric :cvsAvailabilityMetric ;
daq:value "1.0"^^xsd:double
.
:measure2
a dqv:QualityMeasure ;
# when daq:computedOn ranges into a dcat:Distribution/dcat:Dataset,
# dqv:hasQualityMeasure is likely to be the inverse of daq:computedOn ?
# In any case we could remove this statement.
daq:computedOn :myDatasetDistribution ;
daq:metric :csvConsistencyMetric ;
daq:value "0.5"^^xsd:double
.
:cvsAvailabilityMetric
a daq:Metric ;
dqv:hasDimension :availability
.
Issue
8
:csvConsistencyMetric
a daq:Metric ;
dqv:hasDimension :consistency
.
:availability
a daq:Dimension ;
dqv:hasCategory :category1;
.
:consistency
a daq:Dimension ;
dqv:hasCategory :category2
.
Is
there
any
reason
for
turning
the
classes
dqv:Dimension,
dqv:Metric
and
dqv:Category
as
well
as
the
properties
dqv:hasDimension
and
dqv:hasCategory
into
"abstract"
classes
and
properties
as
they
were
defined
in
daQ
(see
Section
"Extending
the
daQ"
here
)?
(
Issue-204
)
# Categories and dimensions might be more extensively defined, for example,
# by grounding them in the section 'Dimensions and metrics hints'.
# However, any quality framework is free to define its own dimensions and categories.
Issue
9
Should
we
represent
dimensions
and
categories
as
instances
of
skos:Concept
?
This
would
allow
publishers
of
quality
framework
to
express
(hierarchical)
relations
between
dimensions
or
categories.
This
could
also
enable
to
align
with
quality-focused
categorizations
less
focused
on
metrics.
Including
the
DWBP
Best
Practices
dimensions,
or
even
the
parts
of
DQV
about
annotations.
(
Issue-205
)
6.2
5.2
Document
the
provenance
of
the
quality
metadata
The
result
results
of
metrics
obtained
in
the
previous
assessment
are
stored
in
the
myQualityMetadata
graph.
# myQualityMatadata is a graph
# we are assuming dqv:QualityMetadata is an extension of daq:QualityGraph,
# otherwise we should probably define a proper qb:DataStructureDefinition
# myQualityMatadata is a graph
:myQualityMetadata {
:myDatasetDistribution
dqv:hasQualityMeasure :measure1, :measure2
.
# The graph contains the rest of the statements presented in the previous example.
}
# myQualityMetadata has been created by: qualityChecker and it is the result of the :qualityChecking activity
:myQualityMetadata
a dqv:QualityMetadata ;
prov:wasAttributedTo :qualityChecker ;
prov:generatedAtTime "2015-05-27T02:52:02Z"^^xsd:dateTime ;
prov:wasGeneratedBy :qualityChecking
.
# qualityChecker is a service computing some quality metrics
:qualityChecker
a prov:SoftwareAgent ;
rdfs:label "a quality assessment service"^^xsd:string
# We should probably suggest to add more info about the services
.
# the qualityChecking is the activity that has generated myQualityMetadata starting from MyDatasetDistribution
:qualityChecking
a prov:Activity;
rdfs:label "the checking of myDatasetDistribution's quality"^^xsd:string;
prov:wasAssociatedWith :qualityChecker;
prov:used :myDatasetDistribution;
prov:generated :myQualityMetadata;
prov:endedAtTime "2015-05-27T02:52:02Z"^^xsd:dateTime;
prov:startedAtTime "2015-05-27T00:52:02Z"^^xsd:dateTime
.
:myQualityMetadata {
:myDatasetDistribution
dqv:hasQualityMeasure :measure1, :measure2
.
5.3
Document
the
provenance
of
single
quality
measurement
}
# myQualityMetadata has been created by: qualityChecker and it is the result of the :qualityChecking activity
:myQualityMetadata
a dqv:QualityMetadata ;
prov:wasAttributedTo :qualityChecker ;
prov:generatedAtTime "2015-05-27T02:52:02Z"^^xsd:dateTime ;
prov:wasGeneratedBy :qualityChecking
.
Note
# qualityChecker is a service computing some quality metrics
:qualityChecker
a prov:SoftwareAgent ;
rdfs:label "a quality assessment service"^^xsd:string
.
# the qualityChecking is the activity that has generated myQualityMetadata starting from MyDatasetDistribution
:qualityChecking
a prov:Activity;
rdfs:label "the checking of myDatasetDistribution's quality"^^xsd:string;
prov:wasAssociatedWith :qualityChecker;
prov:used :myDatasetDistribution;
prov:generated :myQualityMetadata;
prov:endedAtTime "2015-05-27T02:52:02Z"^^xsd:dateTime
prov:startedAtTime "2015-05-27T00:52:02Z"^^xsd:dateTime;
.
The
group
has
discussed
provenance
at
different
level
of
granularity
(dqv:QualityMeasure
and
dqv:QualityMetadata),
so
we
might
consider
to
add
an
example
of
provenance
for
dqv:QualityMeasure.
6.3
5.4
Document
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.
The
HCLS
Community
Profile
for
describing
datasets
provide
provides
further
examples.
6.4
5.5
Express
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 .
5.6
Express
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.csv> ;
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;
rdfs:label "Conflicting concepts"@en ;
rdfs:comment "Number of concepts having conflicting labels"@en ;
dqv:hasDimension :LabelConflicts
.
:numOfConceptsWithIncompleteLanguageCoverage
a dqv:Metric;
rdfs:label "Language incomplete concepts"@en ;
rdfs:comment "Number of concepts having an incomplete language coverage"@en ;
dqv:hasDimension :incompleteLanguageCoverage .
:LabelConflicts
a dqv:Dimension;
rdfs:label "Label Conflicts"@en ;
rdfs:comment "Dimension corresponding to the label conflicts quality issue"@en ;
dqv:hasCategory :labelingDocumentationIssues .
:incompleteLanguageCoverage
a dqv:Dimension;
rdfs:label "Incomplete Language Coverage"@en ;
rdfs:comment "Dimension corresponding to the incomplete language coverage issue"@en ;
dqv:hasCategory :labelingDocumentationIssues .
:labelingDocumentationIssues
a dqv:Category ;
rdfs:label "Labeling and Documentation Issues"@en ;
rdfs:comment "Category grouping labeling and documentation issues"@en ;
.
DQV
represents
the
qSKOS
quality
assessment
on
myControlledVocabulary
for
the
dimensions
“Incomplete
Language
Coverage”
and
“Label
Conflicts”.
(VoID)
linksets
are
collections
of
(RDF)
links
between
two
datasets.
Linksets
are
as
important
as
datasets
when
it
comes
to
the
joint
exploitation
of
independently
served
datasets
in
linked
data.
The
representation
of
quality
for
a
linkset
offers
a
further
example
of
how
DQV
can
be
exploited.
Let’s
define
three
DCAT
datasets,
including
one
VoID
linkset,
which
connects
the
two
others:
: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 between My dataset 1 and My dataset 2";
void:linkPredicate skos:exactMatch ;
void:target :myDataset1 ;
void:target :myDataset2
.
We
can
represent
information
about
the
quality
of
:myLinkset
using
the
“Multilingual
importing”
[
MultilingualImporting
]
linkset
quality
metric.
This
metrics
works
on
linksets
between
datasets
that
include
SKOS
concepts
[
SKOS-reference
].
It
quantifies
the
information
gain
when
adding
the
preferred
labels
or
the
alternative
labels
of
the
concepts
from
a
linked
dataset
to
the
descriptions
of
the
concepts
from
the
other
dataset,
which
these
concepts
have
been
matched
with
a
skos:exactMatch
statement
from
the
linkset.
We
must
first
define
the
proper
metric,
dimension
and
category.
# Definition of instances for Metric, Dimension and Category.
:importingForPropertyPercentage
a dqv:Metric;
dqv:hasDimension :completeness.
:completeness
a dqv:Dimension;
dqv:hasCategory :complementationGain .
:complementationGain
a dqv:Category .
The
quality
assessment
of
the
"label
importing"
can
be
made
dependent
on
two
extra
parameters:
property
and
language,
respectively
the
SKOS
property
and
the
language
tag.
We
extend
DQV
to
represent
these
parameters.
Issue
11
10
This
section
will
be
completed
by
examples
coming
from
Riccardo's
work
on
measuring
We
need
to
further
evaluate
the
way
we
add
extra
parameters
for
the
metric
and
extend
the
DAQ
RDF-CUBE
data
structure
(postponed
issue)
:language
a qb:DimensionProperty, owl:DataProperty ;
rdfs:comment "language on which label importing is assessed."@en ;
rdfs:domain dqv:QualityMeasure;
rdfs:label "label import assessment language"@en .
:property
a qb:DimensionProperty, rdf:Property ;
rdfs:comment "property which label importing is assessed."@en ;
rdfs:domain dqv:QualityMeasure ;
rdfs:label "label import assessment property"@en ;
rdfs:range rdf:Property .
Let
us
add
actual
quality
of
linksets
between
SKOS
concept
schemes,
from
assessments:
:qualityMeasureDataset a dqv:QualityMeasureDataset ;
qb:structure :dsd .
:importingForPropertyPercentage
# should dqv:hasObservation be added as inverse of dqv:hasMetric?
dqv:hasObservation :exactMatchaltLabelit1 , :exactMatchaltLabelit2 ,
:exactMatchaltLabelen1 , :exactMatchaltLabelen2,
:exactMatchprefLabelit1, :exactMatchprefLabelit2 .
#Adding quality observations
## for Italian alternative labels
:measure_exactMatchAltLabelItDataset1
a dqv:QualityMeasure;
dqv:computedOn :myLinkset ;
dqv:value "1.0"^^xsd:double ;
dqv:hasMetric :importingForPropertyPercentage ;
qb:dataSet :qualityMeasureDataset;
:language "it" ;
:property skos:altLabel .
:measure_exactMatchAltLabelItDataset2
a dqv:QualityMeasure;
dqv:computedOn :myLinkset ;
dqv:value "1.0"^^xsd:double ;
dqv:hasMetric :importingForPropertyPercentage ;
qb:dataSet :qualityMeasureDataset;
:language "it" ;
:property skos:altLabel .
## for English alternative labels
:measure_exactMatchAltLabelEnDataset1
a dqv:QualityMeasure;
dqv:computedOn :myLinkset ;
dqv:value "0.1"^^xsd:double ;
dqv:hasMetric :importingForPropertyPercentage ;
qb:dataSet :qualityMeasureDataset;
:language "en" ;
:property skos:altLabel .
:measure_exactMatchAltLabelEnDataset2
a dqv:QualityMeasure;
dqv:computedOn :myLinkset ;
dqv:value "1.0"^^xsd:double ;
dqv:hasMetric :importingForPropertyPercentage ;
qb:dataSet :qualityMeasureDataset;
:language "en" ;
:property skos:altLabel .
## for Italian preferred labels
:measure_exactMatchPrefLabelItDataset1
a dqv:QualityMeasure;
dqv:computedOn :myLinkset ;
dqv:value "0.5"^^xsd:double ;
dqv:hasMetric :importingForPropertyPercentage ;
qb:dataSet :qualityMeasureDataset;
:language "it" ;
:property skos:prefLabel .
:exactMatchprefLabelit2
a dqv:QualityMeasure;
dqv:computedOn :myLinkset ;
dqv:value "0.5"^^xsd:double ;
dqv:hasMetric :importingForPropertyPercentage ;
qb:dataSet :qualityMeasureDataset;
:language "it" ;
:property skos:prefLabel .
Let
us
specify
the
perspective
RDF
Data
Cube
data
structure:
:dsd a qb:DataStructureDefinition ;
##Copying the structure of daq:dsq
qb:component [ qb:dimension dqv:computedOn ;
qb:order 2
] ;
qb:component [ qb:measure dqv:value] ;
qb:component [ qb:dimension <http://purl.org/linked-data/sdmx/2009/dimension#timePeriod> ;
qb:order 3
] ;
qb:component [ qb:dimension dqv:hasMetric ;
qb:order 1
] ;
qb:component [ qb:measure dqv:value;];
# Attribute (here: unit of measurement)
qb:component [
qb:attribute sdmx-attribute:unitMeasure
;
qb:componentRequired false ;
qb:componentAttachment qb:DataSet ; ] ;
##Extending the structure of lds:dsq with two new dimensions
qb:component [ qb:dimension :property ;
qb:order 4
] ;
qb:component [ qb:dimension :language ;
qb:order 5
] .
5.8
Express
the
conformance
of
adding
multilingual
labels
a
dataset's
metadata
with
a
standard
It
is
often
desirable
to
these
schemes.
On
the
Web,
linksets
indicate
that
metadata
about
datasets
in
a
catalogue
are
compliant
with
a
metadata
standard,
or
an
especially
interesting
case
application
profile
of
datasets!
we
could
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
enables
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.
ex:myDataset a dcat:Dataset;
ex:myDatasetRecord a dcat:CatalogRecord ;
foaf:primaryTopic :myDataset ;
dcterms:conformsTo :geoDCAT-AP .
ex:geoDCAT-AP a dcterms:Standard;
dcterms:title "GeoDCAT Application Profile" ;
dcterms:comment "GeoDCAT-AP is developed in the context of the Interoperability Solutions for European Public Administrations (ISA) Programme"@en;
dcterms:issued "201X-XX-XX"^^xsd:date .
Note
that
this
example
does
not
include
the
metadata
about
the
dataset
ex:myDataset
itself.
We
assume
this
is
present
in
an
RDF
data
source
accessible
via
the
URI
ex:myDatasetRecord
.
We
also
add
examples
from
qSKOS
.
assume
that
ex: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.
Issue
11
Relation
between
DQV,
ISO
19115/19157
and
GeoDCAT-AP:
DQV
is
already
able
to
express
the
notion
of
"conformance"
to
a
standard
using
the
property
dcterms:conformsTo.
However,
there
were
suggestion
to
be
further
compatible
with
ISO
19157:2013
and
INSPIRE
by
adding
respectively
"Not
conformant"
and
"Not
evaluated"
as
possible
properties
or
values.
Should
DQV
be
this
expressive?
(
Issue-188
Issue-202
)
7.
6.
Dimensions
and
metrics
hints
This
section
is
non-normative.
Note
This
section
will
be
refined,
especially
considering
public
feedback.
refined
as
soon
as
Issue-204
and
Issue-205
are
solved.
In
particular,
following
the
discussion
on
Issue-200
,
we
plan
to
align
the
DQV
dimension
classification
with
the
ISO
25012
[
ISOIEC25012
]
and
to
provide
the
classification
proposed
in
Zaveri
Et
Al.
[
ZaveriEtAl
]
as
a
further
example.
Suggestions
on
possible
mappings
between
ISO
25012
and
Zaveri
et
al.'s
dimensions
as
well
as
any
other
well-known
classification
are
welcome.
This
section
gathers
relevant
quality
dimensions
and
ideas
for
corresponding
metrics,
which
might
be
eventually
represented
as
instances
of
daq:Dimension
and
daq:Metric
.
The
goal
is
not
to
define
a
normative
list
of
dimensions
and
metrics,
rather,
the
section
provides
a
set
of
examples
starting
from
use
cases
included
in
the
Use
Cases
&
Requirements
document
and
from
the
following
sources:
Are
the
levels
of
granularity
of
dqv:Dimension
and
dqv:Category
well-defined
enough
and
fit
for
purpose?
(
Issue-225
)
7.1
6.1
Statistics
The
following
table
gives
example
on
statistics
that
can
be
computed
on
a
dataset
and
interpreted
as
quality
indicators
by
the
data
consumer.
Some
of
them
can
be
relevant
for
the
dimensions
listed
in
the
rest
of
this
section.
The
properties
come
from
the
VoID
extension
created
for
the
Aether
tool
.
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
Issue
12
Are
statistics
about
a
dataset
a
kind
of
quality
info
we
need
to
include
in
the
data
quality
vocabulary?
(
Issue-164
)
Issue
13
Note
The
Aether
VoID
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
(see
examples
).
This
document
will
probably
have
to
explain
DAQ.
Guidance
on
how
the
different
patterns
DQV/daQ
can
work
with
other
quality
statistics
vocabulary
will
be
reconciled,
if
indeed
both
should
exist
alongside.
(
Issue-189
)
provided.
7.2
6.2
Availability
Can
the
data
be
accessed
now
and
over
time?
Yes/no,
maybe
with
explanation
why
the
data
is
not
available
(privacy,
security,
archived,
lost,
not
yet
captured
etc.)
Open/restricted/registration,
again
possibly
with
explanation
For
access/re-use
Indication
of
persistence
and
longevity
Since
a
dcat:Dataset
is
an
abstract
thing,
it
might
be
available
at
any
point
in
time,
past
present
or
future.
We
already
have
dcterms:issued
so
two
properties
come
to
mind:
dcat:verifiedAvailableOn
{date}
(the
last
time
someone/something
checked
that
the
dataset
was
accessible,
probably
applies
to
a
dcat:Distribution,
not
dcat:Dataset)
dcat:availableUntilAtLeast
{date}
(Potentially
a
data
on
which
the
dataset
is
expected
to
be
withdrawn)
Other
questions
that
come
to
mind:
how
do
we
indicate
that
the
dataset
is
expected
to
be
available
'for
the
foreseeable
future?'
7.3
6.3
Processability
Is
the
data
machine
readable
?
Level
on
the
5-star
scale
(although
there
were
opinions
that
it
is
dangerous
to
attach
value
to
the
linking
because
the
data
might
be
good
but
link
to
‘bad’
data)
Links
to
metadata
standards
used
and
data
model/schema
to
enable
automatic
processing
7.4
6.4
Accuracy
is
the
data
correctly
representing
the
real-world
entity
or
event?
7.5
6.5
Consistency
Is
the
data
not
containing
contradictions?
Can
I
use
it
readily
in
an
analysis
tool?
Can
I
open
the
dataset
in
R
and
do
some
statistical
manipulations?
Can
I
open
it
in
Tableau
and
make
a
visualization
without
doing
a
lot
of
cleaning?
There
could
be
some
overlap
with
accuracy.
7.6
6.6
Relevance
Does
the
dataset
include
an
appropriate
amount
of
data?
It
might
be
useful
to
include
some
information
about
the
context
(e.g.,
why
was
the
data
created
and
what
purpose
is
it
supposed
to
serve).
7.7
6.7
Completeness
Does
the
data
include
all
data
items
representing
the
entity
or
event
?
7.8
6.8
Conformance
Is
the
data
following
accepted
standards
?
7.9
6.9
Credibility
Is
the
data
based
on
trustworthy
sources
?
This
is
described
using
the
provenance
vocabulary
PROV-O
7.10
6.10
Timeliness
Is
the
data
representing
the
actual
situation
and
it
is
published
soon
enough
?
R-DataMissingIncomplete:
'Publishers
should
indicate
if
data
is
partially
missing
or
if
the
dataset
is
incomplete'
R-QualityComparable:
'Data
should
be
comparable
with
other
datasets'
R-Data
should
be
complete:
'Data
should
be
complete'
R-QualityMetrics:
'Data
should
be
associated
with
a
set
of
documented,
objective
and,
if
available,
standardized
quality
metrics.
This
set
of
quality
metrics
may
include
user-defined
or
domain-specific
metrics.'
R-QualityOpinions:
'Subjective
quality
opinions
on
the
data
should
be
supported'
R-GranularityLevels:
'Data
available
at
different
levels
of
granularity
should
be
accessible
and
modeled
in
a
common
way'
We
have
to
confirm
whether
the
scope
of
DQV
work
is
indeed
these
"official"
DQV
reqs
or
if
we
should
go
beyond,
e.g.,
reflecting
the
quality
of
the
vocabulary
(re-)used,
access
to
datasets,
metadata
and
more
generally
the
implementation
of
our
best
practices
(cf.
the
"5
stars"
thread
).
The
distinction
between
intrinsic
Intrinsic
and
extrinsic
metadata
may
help
making
choices
here.
For
example,
DQV
could
be
defined
wrt.
intrinsic
properties
of
the
datasets,
not
extrinsic
properties
(let
alone
properties
of
the
metadata
for
a
dataset!)
(
Issue-190
)
Issue
15
14
Backward
compatibility
with
DAQ
and
RDF
Data
Cube:
DAQ
exploits
Data
Cube
to
make
metric
results
consumable
by
visualizers
visualisers
such
as
CubeViz
(see
Jeremy's
paper
).
This
may
be
useful
to
preserve
in
DQV.
(
Issue-191
)
Issue
15
The
W3C
Human
Care
and
Life
Science
Community
Group
has
created
a
DCAT
profile
for
describing
datasets
.
This
is
work
is
well
visible
and
used
in
the
HCLS
community.
DQV
should
be
aligned
with
this
profile
if
there
are
overlapping
areas.
Are
there
such
areas?
(
Issue-221
)
Revision
of
the
section
"Vocabulary
Specification".
Inclusion
of
"Vocabulary
Specification"
subsections
describing
Dimension,
Category,
Quality
Measure
Dataset,
Quality
Annotation,
User
Quality
Feedback.
Inclusion
of
new
examples
to
express
(i)
that
a
dataset
received
an
ODI
certificate,
(ii)
the
quality
of
SKOS
concept
schemes,
(iii)
the
quality
of
linksets,
(iv)
the
conformance
of
a
dataset's
metadata
with
a
standard.
Revision
of
the
document
according
to
resolved
issues.
Inclusion
of
new
open
issues
arising
from
group
discussion
and
external
feedback.
Creation
of
an
RDF
file
for
DQV
at
http://www.w3.org/ns/dqv#.
