This document summarizes preliminary results from the Contextualization project, which is aiming to disambiguate and link open cultural heritage data. In the first year, several project partners contextualized persons and corporate bodies by identifying global identifiers from sources like GND, VIAF, and Wikidata. The document outlines the data sources and workflow used, and demonstrates the SILK linkage tool for matching entities across structured and unstructured data. It notes some limitations of the current approach and possibilities for improving the contextualization process going forward.

1. Preliminary Results from the
Contextualization
Dominique Ritze, Klaus Thoden
co-funded by the European Union

2. Why contextualization?
•
•
Disambiguation
Linked Open Data

3. Contextualization in Year 1
• Baseline
• Identification of global identifiers
• Authority and type of identity
• BBAW, SBB, NLI, UB Frankfurt, MPIWG, ÖNB:
– mostly contextualization of persons and
corporate bodies
• What can we do more?
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4. Sources
• GND/ VIAF – Persons, corporations, titles
• LCSH, DDC – Subject headings
• Wikipedia/Dbpedia – Everything
• Geonames – Places
• InPho – Argumentation structure
• ISIL – Libraries
• CERL – Historical places, printers
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5. GND
<title>Der zerbrochene Krug</title>
GND
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6

6. Viaf
<author>Ludwig Wittgenstein</author>
IA F
V
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7

7. Wikipedia/Dbpedia
<subfield code=“a“>Aus der Bibliothek des
Prinzen Eugen von Savoyen</subfield>
edia
ikip
W
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8

8. LCSH
<subject>Adminstration</subject>
CSH
L
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9

9. DDC
The 1914 - 1918 Collection of the American Jewish Joint
Distribution Committee is comprised of the records of the
New York headquarters for the period from the Joint's origins
providing emergency relief through World
War I.
DC
D
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10

10. Geonames
<pubPlace>Berlin</pubPlace>
Ge
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es
Nam
o
11

11. Sources
• GND/ VIAF – Persons, corporations, titles
• LCSH, DDC – Subject headings
• Wikipedia/Dbpedia – Everything
• Geonames – Places
• InPho – Argumentation structure
• ISIL – Libraries
• CERL – Historical places, printers
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12. Workflow
• Ingestion through Omnom
• Contextualization in DM2E Triplestore
• Common input vocabulary – but not really
consistent
• Saved as independent triples – no change of
original data
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13. SILK Demo
•
•
•
•
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Workbench to create Linkage Rules with a GUI
Transformations and Normalizations
Similarity metrics to compare values
Aggregators to combine various comparisons
14

14. Structured Data
Unique Identifier
a1
GND
“118650130“
equals
a2
GND
“118650130“
project data
GND
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15. Structured Data
Datatype Properties
a1
name
“C. Brodley“
similarity
a2
name
“Brodley, Carla“
project data
GND
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17

16. Structured Data
Combination of Datatype Properties
a1
year
“1991“
a2
name
name
similarity
“C. Brodley“
“Brodley, Carla“
year
“1991“
similarity
project data
GND
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18

17. Structured Data
Excluding Links
“1956“
year of birth
a1
name
“C. Brodley“
similarity
a2
name
“Brodley, Carla“
year of death
“1820“
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project data
GND
19

18. Unstructured Data
Real-world example
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24

19. Limitations





Needs high computing power
No on-the-fly change of linkage rules
Not well-suited for structured data
Sparse metadata: get information out of
transcriptions? Named Entity Recognition?
Know your data! Results have to be checked.
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20. DM2E Silk Workbench
• Put behind SSO
• No user management
• Keep own sources (at least GND)
• Possibly keep contextualization job to some
power users
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