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Bridging Social Web and Sem Web : 2 application cases in the field of sustainable developpment

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Bridging Social Web and Sem Web : 2 application cases in the field of sustainable developpment

  1. 1. From  folksonomies  to  structured  knowledge  representations:   bridging  Collaborative  Web  and  Semantic  Web   Des  folksonomies  aux  représentations  structurées  de  connaissances:  faire  le  pont  entre  Web  Collaboratif  et  Web   Sémantique   Freddy  Limpens    fdy@pl-­‐area.net   h0p://pl-­‐area.net   A D B S   /   C o o p é r a - o n   &   D é v e l o p p e m e n t   A t e l i e r   W e b   S é m a n - q u e   e t   D é v e l o p p e m e n t   D u r a b l e   –   3 1 . 0 1 . 2 0 1 1   1
  2. 2. From  social  tagging  to  folksonomies   Tags  freely  associated  to  resources  …     …  collected  and  shared  on  the  web   2
  3. 3. …  resul=ng  in   FO LKSO NO M IES   A  mass  of  users  for  a  mass  of  resources   3
  4. 4. Limita-ons  of  folksonomies   Spelling  varia-ons  of  tags:   newyork  =  new_york    =  nyc     4
  5. 5. Limita-ons  of  folksonomies   Lack  of  seman-c   links  between     tags   5
  6. 6. Limita-ons  of  folksonomies   Lack  of   interoperability   between  social   data  repositories     6
  7. 7. How  to  turn     folksonomies  ...   ? pollutant related Energy related pollution ...  into   has narrower  topic  structures  (thesaurus)  ?   Soil pollutions 7
  8. 8. 1.   State  of  the  art   8
  9. 9. State  of  the  art   Involving  users  in  tags  structuring:   •  Simple  syntax  to  structure  tags  (Huyn-­‐Kim   Bang  et  al.  2008)  •  Crowdsourcing  strategy  to  validate  tag-­‐ concepts  mapping  (Lin  et  al.  2010)   pollutant Energy related related pollution has narrower Soil pollutions 9
  10. 10. State  of  the  art   Automa-c  extrac-on  of  tag  seman-cs:   Energy pollutant related related pollution has narrower Soil pollutions 10
  11. 11. Tags  and  Seman-c  Web  models  TAGS  +  SCOT  +  SIOC  +  FOAF    for  tags  and  tagging  :   tags:Tagging   tags:taggedBy    foaf:Agent   #11111   #freddy.limpens   tags:associatedTag   tags:taggedResource   scot:Tag   sioc:Item   #wind-­‐energy   h0p://www.windenergy.com   11
  12. 12. Tags  and  Seman-c  Web  models   What  is  a  tagging  ?   "nature"! (1)   (2)   (3)   picture   shows   "nature"   place   located   l:england   edi=ng   makes  me   :  )   Tagging  =  linking  a  resource  with  a  sign   12
  13. 13. Tags  and  Seman-c  Web  models   NiceTag  (Monnin  et  al,  2010):          Tagging  as  named  graphs*   nt:ManualTagAc=on  (named  graph)   nt:TaggedResource   nt:isAbout   scot:Tag   h0p://www.windenergy.com   #wind-­‐energy   sioc:has_creator   sioc:has_container   sioc:UserAccount   sioc:Container   freddy   delicious.com   *Carrol  et  al.  (2005) 13
  14. 14. Tags  and  Seman-c  Web  models  2  complementary  seman=c  enrichment:   environment   renewable     energy   related   has  broader   wind-­‐energy   close  match   has  narrower   windenergy   wind  turbine   Structuring tags as in a thesaurus (SKOS) 14
  15. 15. 2.  1st  Applica-on  case  :   Corpus  management     at  ADEME   15
  16. 16. Ademe  scenario     Public  audience   Experts   read  +  tag   produce  docs     Archivists   centralize  +  tag   +  tag   16
  17. 17. Folksonomy  enrichment  life-­‐cycle   User-centric structuring Flat Automaticfolksonomy processing Detect ADDING TAGS conflicts Structured Global folksonomy structuring 17
  18. 18. Folksonomy  enrichment  life-­‐cycle   User-centric structuring Flat Automaticfolksonomy processing Detect ADDING TAGS conflicts Structured Global folksonomy structuring 18
  19. 19. 1.  String-­‐based  metrics   pollution Soil pollutions => « pollution » broader than « soil pollutions » pollution pollutant => « pollution » related to « pollutant » 19
  20. 20. 3.  User-­‐based  associa-on   renewable  energy   wind-­‐energy      Claire      Alex      Anne      Delphine      Monique   ⇒   Hyponym  rela=ons  (broader/narrower):      «  renewable  energy  »  broader  than  «  wind-­‐energy  »   20
  21. 21. 3.  User-­‐based    associa-on   21
  22. 22. Computed  rela.ons  are  not  always  accurate    %&"()&$*"&&+)&$#,)--.*/$0"&."*1$&)-"1)($ 22
  23. 23. Folksonomy  enrichment  life-­‐cycle   User-centric structuring Flat Automaticfolksonomy processing Detect ADDING TAGS conflicts Structured Global folksonomy structuring 23
  24. 24. Capturing  userss  contribu-ons     Embedding  structuring  tasks  within  everyday  ac.vity  (searching  e.g)   24
  25. 25. Capturing  userss  contribu-ons     25
  26. 26. Folksonomy  enrichment  life-­‐cycle   User-centric structuring Flat Automaticfolksonomy processing Detect ADDING TAGS conflicts Structured Global folksonomy structuring 26
  27. 27. Conflict  detec-on   John   Anne   hasApproved   hasApproved   narrower environment   pollu=on   broader hasApproved   hasApproved   Monique   Delphine   27
  28. 28.  Experimenta-on  at  ADEME   <1=1&812) :+,) 516787691) !"#$%&#() :;,) *+,) -../"012) 34,) 28
  29. 29. Folksonomy  enrichment  life-­‐cycle   User-centric structuring Flat Automaticfolksonomy processing Detect ADDING TAGS conflicts Global Structured structuring folksonomy 29
  30. 30. Global  map   Includes  all  points  of  view,  highlights  conflicts  +  consensuses   30
  31. 31. Referent  choices   Choices  of  the  referent  user  (archivists  at  Ademe  e.g.)   31
  32. 32. Folksonomy  enrichment  life-­‐cycle   User-centric structuring Flat Automaticfolksonomy processing Detect ADDING TAGS conflicts Structured Global folksonomy structuring 32
  33. 33. Each    point  of  view  corresponds  to  a  layer   33
  34. 34. Enriching  individual  points  of  view   Integra=ng  others  contribu=ons:   Anne  is  looking  for   1.  Current  user  -­‐>  "Anne"   resources  tagged   2.  ReferentUser  (e.g.  archivists)   "environnement"   3.  ConflictSolver  (sohware  agent)   4.  Other  individual  users   5.  Automatons  (metrics)   domaines  environnementaux   BROADER   RELATED   Search:   environnement   NARROWER   CLOSE  MATCH   preoccupa=on  environnementales   environmental   grenelle  de  l  environnement   environment   competences  environnementales   34
  35. 35. 2.  2nd  Applica-on  case  :   Leveraging    the  reuse  of  2nd   hand  objects   35
  36. 36. Paris How  will  I  get  rid  of     all  these  rusty  coffee  makers??   Nice
  37. 37. Digitazing  the  stock  of  2nd  hand  shops   37
  38. 38. Seman-cally  Enhanced  catalog   The goal : Finding semantically ? Related tags ? To enhance searching ? coffee maker
  39. 39. Seman-cally  Enhanced  catalog   The idea : Mapping tags With ontologies’ concepts Hot liquid container coffee pot coffee maker tea pot = subClassOf
  40. 40. Seman-cally  Enhanced  catalog  1.  The  user  enter  "coffee  maker"   Results for "coffee maker": coffee maker2.  The  system  suggests  addi=onal    results  thanks  to  seman=c  rela=ons   Related results : Results for "tea pot": Results for "coffee pot":
  41. 41. 5.  Conclusion   41
  42. 42. What  we  do  :   Help  online  communi=es                                         environment   renewable     energy   related   has  broader   wind-­‐energy   structure  their  tags   related   has  narrower   sustainability   wind  turbine   42
  43. 43. Our  contribu-ons:     An  approach  to  bridge    tagging  with  Seman-c  Web:       Automa-c  processing  of  tags:     User  interface  to  capture  tag  structuring  embedded  in   every-­‐day  tasks     Implementa-on  within  ISICIL  solu=on  (tagging  server)   43
  44. 44. Future  work   •  1st  scenario:   •  More  user  interfaces   •  test  within  ISICIL  (ANR)  project   •  Mul=linguism   •  2nd  scenario:   •  op=miza=on  of  storage  in  the  reuse  of  valued  waste   •  generic  applica=ons   44
  45. 45. Thank  you  for  your  aden-on  !  me  :      freddy.limpens@inria.fr http://www-sop.inria.fr/members/Freddy.Limpensmy  advisors  :      Fabien  Gandon  :  fabien.gandon@inria.fr  Michel  Buffa  :  buffa@unice.frISICIL  team  :    http://isicil.inria.fr 45

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