Presentation of the paper https://dl.gi.de/handle/20.500.12116/3955 at the LEDSPLaY17 Workshop

1. A Method for Distributed and Collaborative Curation of
RDF Datasets Utilizing the Quit Stack
Natanael Arndt and Norman Radtke
September 28, 2017
LEDSPLaY 2017, INFORMATIK 2017, Chemnitz
LEDS
INKED NTERPRISE ATA ERVICESL E D S

2. Collaborative Curation

3. Collaborative Curation
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4. Collaborative Curation
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5. Collaborative Curation
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6. Collaborative Curation
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7. Collaborative Curation
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8. Collaborative Curation
Collaboration
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9. Distributed Collaborative Curation

10. Distributed Collaborative Curation
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11. Distributed Collaborative Curation
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12. Distributed Collaborative Curation
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13. Distributed Collaborative Curation
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14. Distributed Collaborative Curation
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15. Distributed Collaborative Curation
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16. Distributed Collaborative Curation
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17. Distributed Collaborative Curation
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18. Distributed Collaborative Curation
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19. Distributed Collaborative Curation
Distributed
Collaboration 16 / 29

20. Distributed Collaborative Curation: Pfarrerbuch
content editor
(Project Team)
[protected zone]
SPARQL
Endpoint
HTML GUI
[stable]
OntoWiki
Persistency Layer
Backup Model
query, search
add, edit, maintain
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21. Distributed Collaborative Curation: Catalogus Professorum Lipsiensium
synchronize
Model Data (SPARUL)
Linked Data
Linked Data
Partial RDF export
Full RDF export
Backup Model
experienced
web user
content editor
(Project Team)
general
web user
SPARQL
Endpoint
HTML GUI
[stable]
OntoWiki
Persistency Layer
SPARQL
Endpoint
HTML GUI
[experimental]
OntoWiki
Persistency Layer
HTML GUI
[stable]
CPL Frontend
Persistency Layer
OCPY
TOWEL
configure
configure
query, search
add, edit, maintain
getData
query, search
browse, annotate, discuss
synchronize
Model Data
synchronize
Model Data
browse, search
[protected zone]
[public zone]
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22. Distributed Collaborative Curation
Central SPARQL endpoints
• One consolidated status of the data
• Single Point of Failure, Unavailability
• Only trusted access allowed
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23. Distributed Collaborative Curation
• Asynchronous collaboration leads to inconsistency
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24. Distributed Collaborative Curation: Three Layers of Abstraction
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25. Distributed Collaborative Curation: Repository Layer
• Store data at different locations on
the Web
• Synchronization at any time
• Using the Git transfer protocol
• Multiple versioning-branches in
parallel
• Postpone the consolidation and
conflict resolution
• Operations like Fork and Pull
Request
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26. Distributed Collaborative Curation: Store Layer
• Provids a SPARQL endpoint
• Translating SPARQL operations to
Git operations
• Manages the RDF graphs in the
store and the Repository
• Commits Changes using the Git
API
• Concurrent edit operations on a
dataset are resolved
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27. Distributed Collaborative Curation: Domain Specific Layer
• Complex domain models for
domain experts
• Domain specific user interfaces
• Expressed in the language of the
domain.
• Enables reuse of non-collaborative
software in a distributed
collaboration setup.
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28. Application

29. Application
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30. Application
experienced
web user
general
web user
content editor
(Project Team)
[protected zone]
SPARQL
Endpoint
HTML GUI
[stable]
OntoWiki
Persistency Layer
query, search
add, edit, maintain
clone/fetch/push
public + private
Data
Data
Transformation
Tasks (ETL)
add new Data
Legacy Data Sources
[public zone]
any RDF
Editor
Commenting
Interface
Browsing
Interfacequery, search
comment
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31. Conclusion & Future Work

32. Conclusion & Future Work
• We have a prototypical implementation of our concepts: QuitStore1
• Improve our Quit Store implementation
• Implement the Quit architecture for real world problems
• Looking for cool use cases
1
https://github.com/AKSW/QuitStore
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33. Conclusion & Future Work
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THE END
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34. Conclusion & Future Work
Natanael Arndt
<http://aksw.org/NatanaelArndt>
<https://natanael.arndt.xyz/>
arndt@informatik.uni-leipzig.de
Questions?
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