Opening Keynote at the Workshop on Data Quality Management in Wikidata on "Open Data Quality: dimensions, metrics, assessment and improvement​"

1. Open Data Quality:
dimensions, metrics,
assessment and improvement
1
Amrapali Zaveri
Workshop on Data Quality Management in Wikidata Jan 18, 2019
@amrapaliz

2. 2

3. An increasing number of
discoveries are made using other
people’s data
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4. 4
Garbage Garbage
Biggest Challenge: Poor Data Quality
*http://www.ibmbigdatahub.com/infographic/four-vs-big-data
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5. Data Quality Assessment
Dimensions & Metrics
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6. Systematic Literature Review
30 core
articles
Conference - 21
Journal - 8
Masters Thesis
- 1
18 Dimensions
69 Metrics
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7. LDQ Dimensions & Metrics
Quality assessment
for linked data: A
survey. A Zaveri, A
Rula, A Maurino, R
Pietrobon, J
Lehmann, S Auer.
Semantic Web 7 (1),
63-93
300+
citations
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8. LDQ Dimensions & Metrics
•Data Quality: commonly conceived as a multi-
dimensional construct with a popular definition ‘fitness
for use’*.
•Dimension: characteristics of a dataset.
•Metric: or indicator is a procedure for measuring an
information quality dimension.
*Juran et al., The Quality Control Handbook, 1974
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9. LDQ Assessment Goal
Fix data quality issues in given sets of (semantic) data
Such quality issues may
• be in source datasets (e.g., inaccurate or wrong data items, outdated data items)
• result from imperfections of a data integration process (e.g., data items that have
been incorrectly linked with each other)
• reveal themselves only after the data integration (e.g., duplicates, inconsistencies)
Data cleaning may be relevant both, for original datasets before combining/
integrating and for datasets resulting from an integration.
Source: http://www.ida.liu.se/research/semanticweb/events/SemDataMgmtTutorial-Part7-
Cleaning.pdf
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10. 18 LDQ Dimensions
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11. LDQ Dimensions - Accessibility dimensions & metrics
• Availability - extent to which data (or some portion of it) is present, obtainable and
ready for use
• accessibility of the SPARQL endpoint and the server
• dereferenceability of the URI
• Interlinking - degree to which entities that represent the same concept are linked to
each other, be it within or between two or more data sources
• detection of the existence and usage of external URIs
• detection of all local in-links or back-links: all triples from a dataset that have
the resource’s URI as the object
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12. LDQ Dimensions - Intrinsic dimensions & metrics
• Syntactic Validity - degree to which an RDF document conforms
to the specification of the serialization format
• detecting syntax errors using (i) validators, (ii) via
crowdsourcing
• by (i) use of explicit definition of the allowed values for a
datatype, (ii) syntactic rules (type of characters allowed and/
or the pattern of literal values)
•
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13. LDQ Dimensions - Intrinsic dimensions & metrics
• Completeness
• Schema - ontology completeness
• Property - missing values for a specific property
• Population - % of all real-world objects of a particular type
• Interlinking - degree to which instances in the dataset are
interlinked
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14. Data Quality Assessment
Tools
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15. RDFUnit: RDF Unit-Testing Suite
http://aksw.org/Projects/RDFUnit.html
Syntactic
Semantic
Consiste
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16. 16
Crowdsourcing Linked Data Quality Assessment
Crowdsourcing linked data quality assessment
M Acosta, A Zaveri, E Simperl, D Kontokostas, S Auer, J Lehmann ISWC 2013 @amrapaliz

17. Luzzu: QA for LOD
http://eis-bonn.github.io/Luzzu/index.html
2
Asses
3
Clean
4
Store
5
Rank
1
Metric
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18. LDQ Assessment Tools — LODLaundromat
http://lodlaundromat.org/
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19. LDQ Beyond Data — Mapping Quality
Dimou et al. Assessing and Refining Mappings to RDF to Improve Dataset
Quality. ISWC 2015.
https://github.com/RMLio/RML-Validator
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20. LDQ- ShEx Validation
https://www.w3.org/2013/ShEx/Examples/
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21. Data Quality Assessment
Improvement
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22. Data Quality Improvement
• Root cause analysis
• Iterative process
• Ensure high data and metadata quality
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23. W3C
Data
Quality
Vocabulary
https://www.w3.org/TR/vocab-dqv/
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24. Poor quality (meta)data hampers research
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25. 25
*400 AI papers
6% code
30% test data
54% pseudo code
*http://science.sciencemag.org/content/359/6377/725 @amrapaliz

26. 26
Lambin et al. Radiother Oncol. 2013. 109(1):159-64. doi: 10.1016/j.radonc.2013.07.007
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27. If we are ever to realize the full
potential of content we create
then we must find ways to reduce the
barrier to publish, find and reuse their
content in a responsible manner
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28. FAIR Principles
http://www.nature.com/articles/sdata201618
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29. Principles to enhance the value of all digital resources
data, images, software, web services, repositories,…
Developed and endorsed by researchers, publishers,
funding agencies, industry partners.
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30. Improving the FAIRness of digital
resources will increase their quality and
their potential and ease for reuse.
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31. Thank you!
Questions?
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