Data quality is commonly defined as fitness for use. Many data consumers face the problem of identifying the quality of data. Data publishers, on the other hand, often do not have the means to identify quality issues in their data. To make the task for both stakeholders easier, we have developed the Dataset Quality Ontology (daQ) [1]. daQ is a core vocabulary for representing the results of quality benchmarking of a linked dataset. It represents quality metadata as multi-dimensional and statistical observations using the Data Cube Vocabulary. Quality metadata are organised as a self-contained graph, which can be embedded into linked datasets to support quality-based retrieval and ranking. During this talk the discussion will include design issues behind the daQ vocabulary and how it helped evolving the upcoming W3C Data Quality Vocabulary initiative [2], and some ontology quality issues related to ontologies and vocabularies. [1] Debattista, J., Lange, C., & Auer, S. (2014). Representing dataset quality metadata using multi-dimensional views. Proceedings of the 10th International Conference on Semantic Systems, 92-99. [2] https://www.w3.org/TR/vocab-dqv/

1. Dataset Quality Ontology: An
engineering experience
Jeremy Deba*sta
University of Bonn / Fraunhofer IAIS
Germany

2. … who am I
• PhD Student at the University of Bonn
• Originally from Malta (Eny island in the
Mediterranean between Italy and Libya)
deba*s@cs.uni-bonn.de 2

3. … who am I
• B.Sc (Hons) in Computer Science – University
of Malta
– Thesis: CollaboraEve EdiEng and Expert Finding
• M.App Sc in Computer Science – DERI (now
Insight), NaEonal University of Ireland, Galway
– Thesis: Ontology-based rules for User-Controlled
Support in Ubiquitous Environments
deba*s@cs.uni-bonn.de 3

4. … my PhD – the big picture
• Work related to Data Quality (in LD)
– represenEng quality metadata (daQ)
– assessing data quality (Luzzu)
– idenEfying new metrics from standard
vocabularies (like PROV-O) using different
techniques for scalability.

5. … agenda
• DefiniEons of Quality
• Look at some quality aspects re: Ontology Engineering
• Our experience in developing daQ
– contribuEons towards a W3C vocab
• VoCoL as a tool for collaboraEve vocabulary
development
• VOWL as a tool for visual representaEon of ontologies
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6. … quality?
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Robert Pirsig
Joseph Juran
Phillip Crosby

7. Robert Pirsig
… the result of care
Zen and the Art of Motorcycle Maintenance (1974)
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8. … fitness for use
Quality Control Handbook (1974)
Joseph Juran
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9. … conformance to
requirements
Quality is Free : The Art of Making Quality
Certain. Mentor book. (1979)
Joseph JuranPhillip Crosby
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10. … what is quality for you?
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11. … Quality as defined in a dicEonary
D1. how good or bad something is
D2. a characteris1c or feature that someone or
something has
D3. a high level of value or excellence
… defini1ons from h9p://www.merriam-webster.com
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12. … dereferenceability
“Any HTTP URI should be dereferenceable, meaning that HTTP clients can look
up the URI using the HTTP protocol and retrieve a descrip1on of the resource
that is iden1fied by the URI.” – Tom Heath, Chris Bizer: Linked Data Book
(LDEvol)
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?
303 See Other 200 OK
…the process that retrieves a
representa/on of the requested
resource

13. … unknown external ontology used
• Usage of ontologies that cannot be
dereferenced.
– rdfs:domain
– rdfs:range
– rdfs:subClassOf
– …
• Usage of deprecated Classes (classes tagged
with owl:deprecatedClass)
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14. … licencing
“Specify an appropriate open data license. Data (in our case ontology) reuse is
more likely to occur when there is a clear statement about the origin,
ownership and terms related to the use of the published data” – BP for
publishing Linked Data (hjps://www.w3.org/TR/ld-bp/)
• A way to define clear boundaries
• Inclusion of Machine and Human Readable License
to ontology’s meta informaEon
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15. … ontology decleraEon
• Describing an ontology using owl:Ontology
(and voaf:Vocabulary – for LOV inclusion)
– Metadata includes: creator, date modified,
descrip1on, version info, preferred namespace uri,
preferred prefix …
– other provenance informaEon such as history of
changes etc…
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16. … domain and range definiEon
• Open domain-range is not recommended
• Reduces interoperability and thus
“understanding” of resources’ properEes
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17. … ontology hijacking
• RedefiniEon of classes and properEes in a
vocabulary that it is not in its natural
namespace.
– e.g redefining foaf:Person in your own ontology to
be a subclass of a newly defined Person concept.
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18. … consistency checking
• Possible problems when using axioms such as:
– owl:inverseFunc1onalProperty
– owl:disjointClass
– owl:disjointWith
– owl:inverseOf
– …
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19. … other possible measures
• MulElingualism
• Human readable labels and comments
• Interlinking with similar terms/concepts
• Valid Syntax
• Un-typed classes and properEes
• … others?
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20. … the daQ meta-model
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21. … daQ – History
• Describing Quality Metadata in a standardised
manner
• Started around the end of 2013
• Forms the basis of the upcoming W3C Data
Quality Vocabulary (DQV) standard
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22. … daQ – The First Version
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23. … daQ – Subsequent Versions
• Always pen, paper and a simple text editor
– GIT as a versioning control system
• 4 versions before the current version
• Use Case iteraEon tesEng
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24. … daQ – 2nd Version
• Introduced: QualityGraph, and 3 levels of
Abstrac1on (Based on Zaveri et al.
categorisaEon)
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rdfg:Graph QualityGraph
A
B
Category Dimension Metric
rdfs:Resource
hasDimension hasMetric
dateComputed requires
value
xsd:dateTime
computedOn
rdfs:Resource

25. … daQ – AbstracEon
• Hiding Complexity
• DisEncEon between daQ concepts and tangible quality
measure concepts
• Abstract classes cannot be typed (rdf:type), but instead
should be sub-classed (rdfs:subClassOf)
• There is no way to check for abstract class violaEon
unless there is an applicaEon that checks such syntax
errors.
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26. … daq – Why Abstract ProperEes?
• Best PracEce to avoid doubt and ambiguity:
– A metric is ajached to one dimension only.
– A dimension is ajached to one category only.
• Unified view also presented in Zaveri et al.
Data Quality Survey
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27. … daQ – 3rd Version
• Introduced: The Data Cube Vocabulary
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rdfg:Graph QualityGraph
A
qb:DataSet
definesQBDataSet
B
Category Dimension Metric
rdfs:Resource
hasDimension hasMetric
dateComputed
requires
value xsd:dateTime
qb:ObservaDon
hasObservaDon
rdfs:Resource
computedOn
metric
qb:dataSet

28. … daQ – 4th Version
• Modified: QualityGraph; Introduced:
expectedDataType; Added: date to Observa1on
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rdfg:Graph QualityGraph
A
qb:DataSet
B
Category Dimension Metric
rdfs:Resource
hasDimension hasMetric
expectedDataType
requires
value
xsd:anySimpleType
qb:ObservaBon
hasObservaBon
rdfs:Resource
computedOn
metric
qb:dataSet
dc:date

29. … daQ – can a metric return a value
other than a simple data type?
“This property from DAQ is defined to have range
xsd:anySimpleType. While it seems useful to define the expected
data type for a metric, a simple type may too narrow: in many
cases a metric will be determined on a data record or a
subgraph.” – [Bailer Warner 28/10/2015]
W3C DWBP Public Comments List - h9ps://lists.w3.org/Archives/
Public/public-dwbp-comments/2015Oct/0019.html
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30. … daQ - 5th Version
• Introduced: isEs1mate, computedBy
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rdfg:Graph QualityGraph
A
qb:DataSet
B
Category Dimension Metric
rdfs:Resource
hasDimension hasMetric
expectedDataType requires
value
xsd:anySimpleType qb:ObservaBon
hasObservaBon
rdfs:Resource
computedOn
metric
qb:dataSet
dc:date
xsd:boolean
isEsBmate
prov:Agent
computedBy

31. … daQ – Current Version
• Removed: computedBy, qb:Observa1on
Added: daq:Observa1on
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rdfg:Graph QualityGraph
A
qb:DataSet
B
Category Dimension Metric
rdfs:Resource
hasDimension hasMetric
expectedDataType requires
value
xsd:anySimpleType daq:ObservaBon
hasObservaBon
rdfs:Resource
computedOn
metric
qb:dataSet
sdmx-dimension:BmePeriod
xsd:boolean
isEsBmate
xsd:dateTime
qb:ObservaBon
prov:EnBty

32. … involvement in W3C
• W3C Working Group – Data on the Web Best
PracEces
– develop open data ecosystem
– provide guidance to publishers
– foster trust in data
• 3 Deliverables:
– Best PracEces
– Data Quality Vocabulary (DQV)
– Data Usage Vocabulary
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33. … involvement in W3C - DQV
• A meta-model to cover many quality aspects
of a dataset (linked data or not)
• The core component describing quanEtaEve
measures is inspired by daQ
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34. … involvement in W3C - DQV
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35. … involvement in W3C - DQV
• Notable issues (204,205) between daQ and
DQV (hjps://www.w3.org/2013/dwbp/track/issues/xxx) - where xxx is
204 or 205
– Usage of abstract classes and properEes
– Defining Category-Dimension-Metric as subclass
of skos:Concept
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36. … collaboraEve framework
• VoCoL – an IDE for collaboraEve vocabulary development
with VCS integraEon
• A (exchangeable) component based system
– Human Readable Document GeneraEon
– Intelligent Turtle Editor
– EvoluEon Tracker
– Ontology VisualisaEon
– SPARQL Endpoint Service
– Client-Side validaEon before commit to VCS
• Online Demo: hEp://buEerbur06.iai.uni-bonn.de/
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37. … visualising ontologies - VOWL
• VOWL – A visual notaEon for OWL
– IntuiEve
– Self-explaining
– Comprehensible
– Well-specified
– Complete
– Device-independent
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hjp://vowl.visualdataweb.org

38. … VOWL implementaEons
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Protégé Plugin
Web VOWL

39. … daQ in VOWL
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40. … References and Links
• (daQ) - RepresenEng dataset quality metadata using mulE-
dimensional views – J. Deba*sta, C. Lange, S. Auer
• (DQV) - hjps://www.w3.org/TR/vocab-dqv/
• (VoCoL) - hjps://github.com/vocol/vocol
• (Zaveri et al.) - Quality Assessment for Linked Data: A
Survey
• (LDEvol) - hjp://linkeddatabook.com/ediEons/1.0/
• (VOWL) – hjp://vowl.visualdataweb.org
Any SuggesJons? Possible CollaboraJons?
debaMs@cs.uni-bonn.de
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