The CUbRIK histoGrap tool illustrated at Digital Humanities Symposium in Luxembourg, by Lars Wieneke (CVCE)

1. CUbRIK Presentation
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Humanist-Machine Interaction for the
digital humanities
Building the Social graph of the History of European
Integration
DHLU 2013
www.cubrikproject.eu

2. About CUbRIK

European Community's Seventh FrameworkProgram FP7-ICT

15 European partners

Multimedia searchprocessing: Puttinghumans in the loop

Demos: History of Europeand Fashion
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3. CUbRIK Presentation
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Why CUbRIK approach
 Human in the loop added value:
Verification of identities/places/events ambiguous and temporal onlypossible by putting humans in the loop
Integration of multiple perspectives
 CUbRIK as an open toolbox allows
follow-up and extension through
third parties
 “
Vertical”integration:
GUI, components, crowdsourcing
integrated in a platform

4. Specification process: User pull & technology push
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5. The situation today

More and more sources such as images, textsand videos are being digitalized
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How can we extract high-level information fromthem?

Harnessing the effectiveness of machines andthe expertise of (expert) crowds

For starters: 3000 images representing thehistory of European integration, more documentsto come soon
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6. 5
History of Europe App
 Name: HoE App
 Domain: digital humanities
 Target users: researchers in digital humanities
Rationale: software-enhanced social hermeneuticsbased on heterogeneous multimedia archives
Researcher approach
Researchers manually verify information about historical photos
Neglects aggregate information from different sources that could lead to new insights
 CUbRIK solution:
Full pipeline for the discovery of relationships in heterogeneous data
Integration of human and machine computation
Visualization of relationships
Discursive interface enabling the mapping of multiple perspectives
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7. 6
Main Challenges
 The main challenges in the HoE app:
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
 Approach:
Crowd-sourced verification of detected faces (false positives/negatives)
Verification of identities through/places/events in timesocial networks ofexperts
Visual knowledge discovery/exploration
Integrated rights aware crawling and storage
Integrated license and provenance management
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8. CUbRIKin a nutshell
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9. Pipelining the CUbRIKcomponents: Conflict resolution
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Raw content
High level features
(automatic annotations)
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
Q&A
Crowdtask

10. Pipelining the CUbRIKcomponents: Human input from click-workers
Great choice for simple tasks:

Face detection: false positives, false negatives
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Monetary motivation, via www.microtask.com
Poor performance on complex tasks:
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Low resolution images
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Different angles etc.
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Actors recurring over time
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11. Pipelining the CUbRIKcomponents: Human input from experts
Capable of complex tasks:
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In-depth knowledge of key actors
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Context knowledge allows inferences
But: Different motivational models!
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Public goods
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Reputation
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12. Pipelining the CUbRIKcomponents: Towards a community of experts
Time for a Demo!
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13. Usage for historians
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No one truth in history but interpretation, context and discussion
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Therefore need to represent ambivalence, contradictions and discussion
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Close ties between data representation (Socialgraph) and their original context (primarysources)
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14. Conclusion& Outlook
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Challenges

Whatistruth? Humanitiesvs. Computer Science
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Gatheringrequirementsfortoolsthathaven‘tbeendevelopedyet
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Engagingcrowds
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Image copyrights
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Refinementoftheapplication
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Additional datasources
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Improvementoftheinterface
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Integration ofthedifferent components
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15. WWW.CUBRIKPROJECT.EU
Visituson
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@CUBRIKPROJECT
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