Download - Humanist machine interaction with histoGraph
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Humanist-Machine Interaction for thedigital humanitiesBuilding the Social graph of the History of European Integration
DHLU 2013
www.cubrikproject.eu
About CUbRIK
European Community's Seventh FrameworkProgram FP7-ICT
15 European partners Multimedia search
processing: Puttinghumans in the loop
Demos: History of Europeand Fashion
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Why CUbRIK approach
Human in the loop added value: Verification of identities/places/events ambiguous and temporal only
possible by putting humans in the loop Integration of multiple perspectives
CUbRIK as an open toolbox allowsfollow-up and extension throughthird parties
“Vertical” integration:GUI, components, crowdsourcing
integrated in a platform
Specification process: User pull & technology push
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The situation today
More and more sources such as images, textsand videos are being digitalized
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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History of Europe App Name: HoE App Domain: digital humanities Target users: researchers in digital humanities Rationale: software-enhanced social hermeneutics
based 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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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 time social networks of
experts Visual knowledge discovery/exploration Integrated rights aware crawling and storage Integrated license and provenance management
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CUbRIK in a nutshell
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Pipelining the CUbRIK components:Conflict resolution
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Raw contentHigh 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&ACrowdtask
Pipelining the CUbRIK components:Human input from click-workers
Great choice for simple tasks: Face detection: false positives, false negatives Monetary motivation, via www.microtask.com
Poor performance on complex tasks: Low resolution images Different angles etc. Actors recurring over time
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Pipelining the CUbRIK components:Human input from experts
Capable of complex tasks: In-depth knowledge of key actors Context knowledge allows inferences
But: Different motivational models! Public goods Reputation
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Pipelining the CUbRIK components:Towards a community of experts
Time for a Demo!
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Usage for historians
No one truth in history but interpretation,context and discussion
Therefore need to represent ambivalence,contradictions and discussion
Close ties between data representation (Socialgraph) and their original context (primarysources)
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Conclusion & Outlook
Challenges What is truth? Humanities vs. Computer Science Gathering requirements for tools that haven‘t been
developed yet Engaging crowds Image copyrights
Refinement of the application Additional datasources Improvement of the interface Integration of the different components
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