Presentation held at Heyne Haus, Göttingen,18/12/2015 in Oberseminar Datenmanagement, Cloud und e-Infrastructure

1. Metadata Quality Assurance
Péter Király
peter.kiraly@gwdg.de
Heyne Haus, Göttingen, 18/12/2015
Oberseminar Datenmanagement, Cloud und e-Infrastructure
Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen

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What is metadata?
 Data about data
 Specifically: descriptive data about ...
 digitized (or physical) object
such as paintings, books, photos
 larger datasets
such as research data
 Provides access points to the underlining data

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Why data quality is important?
„Fitness for purpose”
no metadata no access to data no data usage
more explanation:
Data on the Web Best Practices
W3C Working Draft 17 December 2015
http://www.w3.org/TR/2015/WD-dwbp-20151217/

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Symptoms of bad quality metadata
 Hard to identify („What it is?”)
 Hard to distinguish from other records
 Misleading descriptions
 Uninterpretable descriptions
 Missing fields
 Unreusable (lost original context)
 Hard to find

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Some typical issues
 Title is not informative

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Mixing different data types
 Numeric
 RDF resource

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Field overuse
 What is the meaning of the field?
 identifier
 relation
 source
TextGrid OAI-PMH response

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Copy & paste cataloguing
 Keeping placeholders / templates

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Same entity, differently recorded
 lucas cranach der ältere
 Cranach, Lucas (der Ältere) [Herstellung]
 Cranach, Lucas (I) (naar tekening van)
 Cranach, Lucas vanem (autor)
Result of entity detection:
 http://dbpedia.org/resource/Lucas_Cranach_t
he_Elder
 http://viaf.org/viaf/49268177/
 none

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Same entity recorded differently
Different displays, and content:
 http://dbpedia.org/resource/Lucas_Cranach_t
he_Elder
 http://viaf.org/viaf/49268177/
 none

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What to measure?
field1 field2 field3 field4
doc1
doc2
doc3
doc3
An overall value for a record

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What to measure?
field1 field2 field3 field4
doc1
doc2
doc3
doc3
An overall value for a record set (e.g. a collection from the same
source)

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What to measure?
field1 field2 field3 field4
doc1
doc2
doc3
doc3
An overall value for a field – how users utilize the field?

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What to measure?
field1 field2 field3 field4
doc1
doc2
doc3
doc3
Field group. A group of fields together supports a given
funtionality, e.g. display, search, identify, re-use, multilinguality.

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Grouping fields by functionalities
Mandatory
Descriptiveness
Searchability
Contextualisation
Identification
Browsing
Viewing
Re-Usability
Multilinguality
dc:title × × × × ×
dcterms:alternative × × × ×
dc:description × × × × × ×
dc:creator × × × ×
dc:publisher × ×
dc:contributor ×
Created by Valentine Charles, Europeana Research and Development team

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Metrics
The foundational metrics were set by Bruce–
Hillmann, Stvilia, Ochoa–Duval, Gavrilis et al.
 Completeness
 Accuracy
 Conformance to expectations
 Logical consistency and coherence
 Accessibility
 Timeliness
 Provenance

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Data sources
 Europeana – the European digital library,
museum and archive: 48M+ medatata records
in EDM (Europeana Data Model) schema
 TextGrid repository: Dublin Core metadata
and TEI (Text Encoding Initiative) records
 Research data from the Göttingen Campus
 Library catalogue records in MARC (Machine
Readable Catalog) schema
 Other open data

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Method: collection – measuring – sharing
 Data collection (ingestion) via REST API, OAI-
OMH harvesting, file download etc.
 Issues:
 GWDG cloud: 160 GB, Europeana: 300 GB
 low I/O performance
 Europeana OAI-PMH is in a „beta” state
 OAI-PMH requires 10M+ HTTP requests
 REST API requires 50M+ HTTP requests

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Method: collection – measuring – sharing
Measuring records
 Big data so it should be scalable
 Apache Hadoop: MapReduce and friends
 Plugable architecture: „meters”
 UI: set parameters for meters
 input: records, schema, meters, config files
 output:
 identifier, projected metadata fields
 metric1, metric2, metric3 ... metricN

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Method: collection – measuring – sharing
Statistical analysis
 Calculating descriptive statistics with
R/Julia/other tool
 Derivation of numbers representing
collections and fields from the record level
measurements

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Method: collection – measuring – sharing
Completeness of 3 collections 2 response types
best in
collection
worst in
collection
similar records
heterogenious
records
different
manifestations

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Method: collection – measuring – sharing
outputs
 Display results in an interactive dashboard
 REST API to share the raw data
Images: i) European Data Portal Metadata Quality Dashboard ii) Kibana promotional video

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Method: collection – measuring – sharing
Data Quality Vocabulary (W3C Working Draft)
http://w3c.github.io/dwbp/vocab-dqg.html
: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 .

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What it is good for?
 Improve the metadata
 Improve metadata schema and its docum.
 Propagate „good practice”
 Improve services: „good” data is ranked
higher in search result list
Specifically for GWDG:
 Could be built in to current and planned data
management / data archiving tools

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Further steps
 Define meters by Domain Specific Language
 Pattern discovery, machine learning,
clustering
 Connectors for data sources
 „Jenkins for data publication”
Problem catalogue
Data source
Schema
Metadata QA Report

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Follow me
 Project plan and blog: http://pkiraly.github.io
 Software development:
 https://github.com/pkiraly/europeana-oai-pmh-client:
Harvester for Europeana OAI-PMH Service
 https://github.com/pkiraly/oai-pmh-lib: OAI-PMH client
library
 https://github.com/pkiraly/europeana-api-php-client: PHP
client for Europeana’s REST API
 https://github.com/pkiraly/europeana-qa: Europeana
Metadata Quality Assurance Toolkit
 @kiru, https://www.linkedin.com/in/peterkiraly