The CUbRIK histograph application illustrated at the EMPOLIS Executive Forum 2014, Berlin, by Lars Wieneke (CVCE)

1. History of Europe
A case study in digital humanities

2. Agenda
• The CVCE
• What are the Digital Humanities?
• The DHLab at the CVCE
• Vision: From image collection to Social Graph
• The CUbRIK approach
• Demo
• Challenges, Lessons learned & Outlook
2

4. 4
www.cvce.eu
09/04/2014 – History of Europe. A case study in digital humanities
Dr.-Ing. Lars Wieneke, Head of Information & Technology, CVCE Luxembourg
www.cvce.eu

5. 5
www.cvce.eu
09/04/2014 – History of Europe. A case study in digital humanities
Dr.-Ing. Lars Wieneke, Head of Information & Technology, CVCE Luxembourg
www.cvce.eu

6. 6
www.cvce.eu
09/04/2014 – History of Europe. A case study in digital humanities
Dr.-Ing. Lars Wieneke, Head of Information & Technology, CVCE Luxembourg
www.cvce.eu

7. 7
What are the digital humanities?

8. „[…] the issue would be not how much computing
we need for getting the answers, but how much
computer science needs us to ask the right
questions.“
http://whatisdigitalhumanities.com, Domenico Fiormonte, Université Roma Tre
8
What are the Digital Humanities?
Digital Humanities is the application of
computational methods and tools for the humanities
but

9. 9
What are the Digital Humanities? Challenges
F.Kapplan, EPF Lausanne
Venice
Fall
Digital
Humani3es
School
2013

10. 10
What are the Digital Humanities? Challenges
F.Kapplan, EPF Lausanne
Venice
Fall
Digital
Humani3es
School
2013

11. 11
The DHLab at the CVCE
European
Integra3on
Studies
Humani'es
DHLab
Development
&
Opera3ons

12. 12
Our vision: Building a social graph from image collections

13. 13
Building a social graph from image collections

14. Building a social graph from image collections

15. 15
The CUbRIK Approach

16. The CUbRIK approach
• European Community's Seventh Framework
Program FP7-ICT
• 15 European partners
• Multimedia search
processing: Putting
humans in the loop
• Combination of human and machine
computation

17. The CUbRIK approach: four pillars
Researcher
Requirements
En3ty
Repository
Efficient
Indexa3on
Process
Toolchain
for
visualiza3on
and
analysis

18. Sourcing researcher requirements
Requirements
User
pull
Technology
push

19. Sourcing researcher requirements
Users Requirements Technology
Selection of target
user group
First draft of the app
scenario
Feedback on
technical scope
Exploratory
interviews
(daily work practices)
Second draft of the
app scenario
Focus group
(user needs and app
scenarios) Feedback on
technical feasability
Lessons learned:
issues and features
Specification
Implementation 1.
demonstrator
Workshop: Review of
app and features
Revised specification
Implementation 2.
demonstrator
Evaluation and test
Stage 1
Stage 2
Stage 3
Stage 4
Stage 5
Users Requirements Technology

20. Building an Entity Repository

21. Efficient indexation
Raw content
Conflict
(e.g., “Image contains
‘Romano Prodi’ ”
? Confidence = low)
Conflict store Conflict
manager
Conflict resolution
task store
Conflict resolution
task: conflict,
required skill, priority, ..
CUbRIK app
for Conflict
resolution
Game Crowdtask Q&A

22. Efficient indexation
Face
detection
Face
identification
Clickworkers
Crowd Face
position
validation
Expert
validation
Expert
Crowd
Collection
ingestion
Social Graph
creation
SMILA

23. Demo

24. Challenges
• Main challenges
– Detection and identification of identities/places/events in time
– Verification of identities/places/events in time
– Analysis of relationships (e.g. co-occurrences)
– Rights aware crawling and storage
– Verification of provenance and license information
– Truth and provenance
• Approach
– Crowd-sourced verification of detected faces (false positives/negatives)
– Verification of identities through/places/events in time social networks of experts
– Visual knowledge discovery/exploration
– Integrated rights aware crawling and storage
– Integrated license and provenance management

25. Lessons learned
• No one truth in history but interpretation, context and discussion
• Therefore need to represent ambivalence, contradictions and discussion
• Close ties between data representation (Social graph) and their original
context (primary sources)

26. Outlook
• Integration of other document types
• Improvement of the interface
• Pre-filtering of identities
• Gamification and social reputation for expert annotation

27. 27
Thank you for listening
29/09/2011 – Title

28. 29/09/2011 – Titre 28