"Stories" in data and the roles of crowdsourcing – views of a Web miner

1. "Stories" in data and the roles of crowdsourcing – views of a Web miner Bettina Berendt Dept. of Computer Science KU Leuven, Belgium http://people.cs.kuleuven.be/~bettina.berendt/ Thanks to: Ilija Subašić, Markus Luczak-Rösch, and Laura Drăgan

3. A story

4. Story structure

5. One case of provenance

6. Another case of provenance

7. Formalizing provenance: a high-level view

8. Challenge 1:Many voices

9. Challenge 2

10. Challenge 3:subjectivity

11. The STORIES Tool

12. Uncover (1)

13. Uncover (2)

14. Scan (over time)

15. Uncover

16. Zoom

17. Search: formulating ad-hoc concepts

18. Track (2)

19. Textual summarization

20. Challenge 4

21. Crowd-sourcing the truth? Wikipedia (here: the Gaza Flotilla Raid)

22. Challenge 5

23. Challenge 5: vagueness - reprise Challenge 4: More specifically

24. The “live crowdsourcing activity“ • Goal: crowdsource data citation metadata • Motivation 1 / possible extension • Motivation 2 / case study

26. http://prov.usewod.org

27. The data Datasets Publications [People]

28. The datasets Preloaded: USEWOD datasets DBpedia SWDF Bio2RDF LinkedGeoData BioPortal OpenBioMed

29. The datasets Preloaded: Generic (!) Versions/releases References

30. The datasets Add new: Name* Version Release date URL

31. The publications Preloaded: USEWOD workshop papers

32. The publications Add new: Title* Authors Year URL

33. The data

34. The task Capture which dataset is used in which publication and how

35. Data representation Datasets Publications Connections between them schema.org prov:Entity ?

36. Data representation Datasets Publications Connections between them schema.org prov:Entity prov:Derivation

37. The task Capture which dataset is used in which publication and how

38. Connections Publication – Publication Publication – Dataset Dataset – Publication Dataset - Dataset

39. Connections Publication – Publication citation

40. Connections Publication – Dataset Dataset – Publication mentions describes evaluates analyses compares

41. Connections Dataset – Dataset extends includes overlaps transformation of generalisation of

42. Data representation Subclasses of prov:Derivation (inverse of Publication-DS)

43. The task Capture which dataset is used in which publication and how

44. Data representation

45. Data representation

46. Bundles

47. Live crowdsourcing activity 2014: outcomes

48. Lessons learned Data is dirty even coming from experts Focus on the task make everything else simpler minimise data input

49. Questionnaire results Inconclusive results on the suitability of the vocabulary, But interesting answers to: „“what questions would this information answer for you?“: “What are popular datasets?” “Which datasets are facilitators for research on X?” “What publications are related through a dataset (but don't mention each other)?”

50. Outlook (1): Dimensions of crowdsourcing What is outsourced Who is the crowd How is the task designed How are the results validated How can the process be optimised [Quinn & Bederson, 2012]