This paper presents our approach to collaborative and semiautomated semantic structuring of folksonomies. Tags freely provided by users of online communities are not semantically linked, and this hinders signicantly the potentials for browsing and exploring these data. We propose a sociotechnical system combining automatic handlings of tags, using state of the art algorithm, and user friendly interfaces designed after a careful analysis of the usage of our target communities. Much like folksonomies, our socio-technical system lets each user maintain his own view while still bene ting from others contributions. As a complement to similar approaches, our approach supports conflicting point of views all along the life-cycle of semantically enriched folksonomies.

1. 1 Helping online communities to semantically enrich folksonomies Freddy Limpens, Fabien Gandon Edelweiss, INRIA Sophia Antipolis {freddy.limpens,fabien.gandon}@inria.fr Michel Buffa Kewi, I3S, Université Nice-Sophia Antipolis Edelweiss WebScience 2010, Raleigh, NC, USA, 26-27 april 2010

2. 2 Social tagging for bookmarking http://delicious.com

3. 3 … and the resulting FOLKS - ONOMIES

4. 4 Spelling variations of tags newyork = new_york = nyc

5. 5 Amibiguity of tags .. in France ? … or in Texas ?

6. 6 How to turn folksonomies ... ? Energy pollutant related related pollution ...into comprehensible topic structures ? has narrower Soil pollutions

7. 7 … by collecting all user's expertise into the process

9. Supporting diverging points of view

11. 10 Supporting diverging points of view skos:related pollution car

12. 11 Supporting diverging points of view skos:related pollution car disagrees agrees John Paul

13. 12 Supporting diverging points of view tagSemanticStatement (named graph) skos:related pollution car hasApproved hasRejected Paul John

14. Folksonomy enrichment life-cycle Flat folksonomy User-centric structuring Automatic processing Detectconflicts ADDING TAGS Global structuring Structuredfolksonomy

15. 14 pollution pollution pollution pollution pollution Soil pollutions pollution pollutant 1. Comparing Tag labels with string edit distances

17. Combining the best metrics

19. tagsFig. Markines et al. (2009)

20. 17 Result: automatically suggested semantics Environment related pollutant Energy related related pollution has narrower Soil pollutions

21. Folksonomy enrichment life-cycle Flat folksonomy User-centric structuring Automatic processing Detectconflicts ADDING TAGS Global structuring Structuredfolksonomy

22. 19 Embedding structuring tasks within everyday activity (searching e.g)

23. 20 Embedding structuring tasks within everyday activity (searching e.g)

24. 21 Capturing user's point of view

25. Folksonomy enrichment life-cycle Flat folksonomy User-centric structuring Automatic processing Detectconflicts ADDING TAGS Global structuring Structuredfolksonomy

26. 23 Conflict detection narrower pollution environment broader

27. 24 Conflict detection narrower pollution environment broader Using rules e.g: IFnum(narrower)/num(broader) ≥ c THENnarrowerwins ELSE 'more generic' wins

28. 25 Conflict detection narrower pollution environment related broader related more generic more generic narrower broader

29. Folksonomy enrichment life-cycle Flat folksonomy User-centric structuring Automatic processing Detectconflicts ADDING TAGS Global structuring Structuredfolksonomy

30. 27 Global structuring by Referent related pollution environment hasApproved ReferentUser

31. Folksonomy enrichment life-cycle Flat folksonomy User-centric structuring Automatic processing Detectconflicts ADDING TAGS Global structuring Structuredfolksonomy

32. 29 Take away message (conclusion)

33. 30 Help communities structure their tags What we do :

35. Automatic processing of tags

36. Tag structuring embedded in every-day tools

38. Tag server

40. More ontologicalresources

41. Other interfaces (tagging, global structuring)