Presentation held by Gildas Illien and Sébastien Peyrard at EuropeanaTech 2015 in Paris

1. D A T A Q U A L I T Y I N T H E A G E O F L I N K E D D A T A
Trust me if you dare
http://gallica/ark:/12148/btv1b90519196
G I L D A S I L L I E N , B N F S É B A S T I E N P E Y R A R D , B N F

2. data.bnf.fr : one vision, three goals
Be reusable
Be visible
Be legible
https://www.flickr.com/photos/ramonbaile/2274662139
http://commons.wikimedia.org/wiki/File:Carte_M%C3%A9tro_de_Pari
s.jpg#mediaviewer/File:Carte_M%C3%A9tro_de_Paris.jpg
By humans
By machines
https://www.flickr.com/photos/bdesham/2432400623
2

3. DATA.BNF.FR: WHAT IT DOES

4. data.bnf.fr: a guided tour
4

14. DATA.BNF.FR: HOW IT WORKS

15. Any black Magic?
1xx
(creator of)
0070
(author)
WORK
PUBLICATION
PERSON
INTERMARC
INTERMARC
INTERMARC
FRBNF11896956
FRBNF11967514
FRBNF37465618

16. Any black Magic?
dc:creator
WORK
PUBLICATION
PERSON
http://catalogue.bnf.fr/ark:/12148/cb118969563
http://catalogue.bnf.fr/ark:/12148/cb11967514v
http://catalogue.bnf.fr/ark:/12148/cb374656186
dc:creator
RDF triples
RDF triples
RDF triples rdarelationships:workManifested

17. STRUCTURED
DATA
VINTAGE
LINKED DATA
SINCE 1987
TRUSTED
IDENTIFIERS
The true magic behind this is:
CATALOGERS AND
CATALOGUE QA
TEAM :
- INTELLIGENT
DATA
- CONSISTENCY
- LINK CURATION
https://www.flickr.com/photos/bohman/4394901689

18. Work-manifestation links: machine calculated
dc:creator
WORK
PUBLICATION
PERSON
http://catalogue.bnf.fr/ark:/12148/cb118969563
http://catalogue.bnf.fr/ark:/12148/cb11967514v
http://catalogue.bnf.fr/ark:/12148/cb374656186
dc:creator
RDF triples
RDF triples
RDF triples rdarelationships:workManifested

19. DATA.BNF.FR : RETURN ON INVESTMENTS?

20. Why not give the data back to
the source catalogue?
MARC
catalogue
data.bnf.fr
structured data
enriched data

21. How to make it happen?
 Start easy
 evolution vs. revolution
 one challenge at a time
 Speak the language of the
catalogue
 channel the skills and
tools of the QA experts
 leverage the organization
around the catalogue

22. What happened… and what will happen
 Tests: improve the
algorithm
 discussion with experts
 injection whenever one
decision
 tolerance of a certain level
of error
 Use the algorithm to
suggest
 suggest multiple
candidates to an expert
when undecicable
 Next steps: new
algorithms
 Aggregates (one
manifestation, many
works)
 Create new works out of
clusters
 Deduplication of sparse
records

23. M I T T L E R Z W I S C H E N H I R N U N D H Ä N D E N
M U S S D A S H E R Z S E I N
(temporary)
conclusion
to be continued…