PhD defense : Multi-points of view semantic enrichment of folksonomies

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This thesis, set at the crossroads of Social Web and Semantic Web, is an attempt to bridge Social tagging-based systems with structured representations such as thesauri or ontologies (in the informatics sense). Folksonomies resulting from the use of social tagging systems suffer from a lack of precision that hinders their potentials to retrieve or exchange information. This thesis proposes supporting the use of folksonomies with formal languages and ontologies from the Semantic Web. Automatic processing of tags allows bootstraping the process by using a combination of a custom method analyzing tags' labels and adapted methods analyzing the structure of folksonomies. The contributions of users are described thanks to our model SRTag, which allows supporting diverging points of view, and captured thanks to our user friendly interface allowing the users to structure tags while searching the folksonomy. Conflicts between individual points of view are detected, solved, and then exploited to help a referent user maintain a global and coherent structuring of the folksonomy, which is in return used to garanty the coherence while enriching individual contributions with the others' contributions. The result of our method allows enhancing the navigation within tag-based knowledge systems, but can also serve as a basis for building thesauri fed by a truly bottom up process.

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PhD defense : Multi-points of view semantic enrichment of folksonomies

  1. 1. Multi-points of 
 view semantic 
 enrichment of folksonomies" 1P h . D T h e s i s d e f e n s e – O c t o b e r 2 5 t h 2 0 1 0 Freddy Limpens Edelweiss, INRIA Sophia Antipolis Edelweiss   Picasso  129ieth  birthday   Supervisors Fabien Gandon, Edelweiss, INRIA Sophia Antipolis Michel Buffa, Kewi/I3S, UNSA/CNRS
  2. 2. 1.  Context  and   mo-va-ons   2
  3. 3. •  Online  communi7es  of  interest   •  "Enterprise  2.0"  &  organiza7ons   ⇒ Cross-­‐fer7lizing  Web  2.0  and   Seman7c  Web   Context  of  the  thesis   3
  4. 4. •  Tools  for  techno/science  monitoring   •  Experts  seeking   •  Industrial  partners:   •  Academic  partners:     Context  of  the  thesis   4
  5. 5. 5 From  social  tagging  to  folksonomies   Tags  freely  associated  to  resources  …     …  collected  and  shared  on  the  web  
  6. 6. 6 …  resul7ng  in   FOLKSONOMIES   A  mass  of  users  for  a  mass  of  resources  
  7. 7. Limita-ons  of  folksonomies   7 Spelling  varia-ons  of  tags:   newyork  =  new_york    =  nyc    
  8. 8. Limita-ons  of  folksonomies   8 Ambiguity  of  tags   …  or  in    Texas,  USA  ?   …  in  France  ?   paris  
  9. 9. Lack  of  seman-c   links  between     tags   Limita-ons  of  folksonomies   9
  10. 10. 10 How  to  turn     folksonomies  ...   ? ...  into    topic  structures  (thesaurus)  ?   pollution Soil pollutions has narrower pollutant Energy related related
  11. 11. 11 …  without  overloading  users   … and by collecting all user's expertise into the process
  12. 12. Outline  of  the  presenta-on   12 1. Context  and  mo7va7ons   2. State  of  the  art  and  posi7oning   3. Tagging  &  folksonomy  enrichment   models   4. Folksonomy  enrichment  life-­‐cycle  
  13. 13. 2.   State  of  the  art   and  posi-oning   13
  14. 14. 14 State  of  the  art   Automa-c  extrac-on  of  tag  seman-cs:   •  Similarity  based  on  co-­‐occurrence  paZerns  (Specia  &  MoZa  2007;   CatuZo  2008)   •  Associa7on  rule  mining  (Mika  2005;  Hotho  et  al.  2006)     pollution Soil pollutions has narrower pollutant Energy related related
  15. 15. 15 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)   •  Integrate  ontology  maturing  into  Social   Bookmarking  tool  (Braun  et  al.  2007)   pollution Soil pollutions has narrower pollutant Energy related related a relation, depending on the actual context. This fact is acknowledged by many ontology formalisms that al- low metamodeling. Using imagenotions, users do not need to understand this somewhat artificial separation of notions. 2. Because imagenotions are associated with images, they are meaningful internationally as an image has the same meaning in different languages. The goal of our methodology is to guide the process of creating an ontology of imagenotions. The main steps of this methodology is based on the ontology maturing process model: 1. Emergence of Ideas. In this step, new imagenotions are created. Already this step can become collaborative, as users can jointly collect the tags describing imageno- tions, and select the most representative images for an imagenotion. Collaborative editing is especially use- ful in a multi-lingual environment where it cannot be expected that any individual user speaks all required languages. 2. Consolidation in Communities. Because it is so easy to create new imagenotions, it cannot be avoided that for the same semantic notion initially many imagenotions are created (synonyms, also in different languages) or that an imagenotion represents more than one seman- tic notion (homonyms). In this step, these problems should be solved by merging synonymous imageno- tions, and by splitting imagenotions representing more than one notion. We now demonstrate some functionality of the tool in terms of the steps of our development methodology. 4.3.1 Step 1: Emergence of Ideas Figure 2 shows an example for the emergence of ideas. Let us assume that a content owner has new images about elephants. The imagenotion “elephant” was so far not avail- able. Therefore, she creates a new imagenotion, adds an image or part of an image that shows elephants and starts describing the new imagenotion with more details. She uses English as spoken language. As synonyms, she enters “ele- phantidae” and “tusker”. Instead of tagging the new images that show elephants with these words, she can use the new imagenotion—she just pulls this imagenotion over the new images via drag and drop. Figure 2: Editing an imagenotion with the No- tionEditor tool
  16. 16. 16 State  of  the  art   Tags  and  Seman-c  Web  models   •  SCOT  for  tags  and  tagging  (Kim  et  al.  2007):  
  17. 17. 17 State  of  the  art   Tags  and  Seman-c  Web  models   •  SCOT  for  tags  and  tagging  (Kim  et  al.  2007):   •  MOAT  (Passant  &  Laublet,  2008)  :  Raising  ambiguity  by  linking   tags  to  concepts  from  Linked  Data  
  18. 18. 18 Posi-oning   Computed   Tag  similarity   Tag-­‐Concept   mapping   Users'   contrib.   Sem-­‐Web   formalism   Mul7-­‐points   of  view   Angeletou  et  al.   (2008)   ✓   ✓   ✓   Huynh-­‐Kim  Bang   et  al.  (2008)   ✓   ✓   Passant  &  Laublet (2008)   ✓   ✓   ✓   Lin  &  Davis   (2010)   ✓   ✓   ✓   ✓   Braun  et  al.   (2007)   ✓   ✓   Our  approach   ✓   ✓   ✓   ✓  
  19. 19. 3.  Tagging  &  folksonomy   enrichment  models   19
  20. 20. 20 Tagging  model   Tagging  =  linking  a  resource  with  a  sign   What  is  a  tagging  ?   "nature"! picture   shows   "nature"   (1)   (2)   (3)   place   located   l:england   edi7ng   makes  me   :  )  
  21. 21. 21 Tagging  model   NiceTag  (Monnin  et  al,  2010):          Tagging  as  named  graphs*   nt:TaggedResource   rdfs:Resource  nt:isRelatedTo   nt:TagAc7on(named  graph)   sioc:UserAccount   sioc:has_creator   sioc:Container   sioc:has_container   xsd:Date   dc:date   *Carrol  et  al.  (2005)
  22. 22. 22 Tagging  model   No  constraints  on  the  model   of  the  sign  used  to  tag   nt:TaggedResource   rdfs:Resource  nt:isRelatedTo   nt:TagAc7on(named  graph)   nt:TaggedResource   hZp:geonames.org/2990440   nt:isRelatedTo   scot:Tag   :)   skos:Concept   nt:isRelatedTo   nt:isRelatedTo   nt:isRelatedTo   nt:isRelatedTo   moat:Tag   moat:hasMeaning  
  23. 23. 23 Tagging  model   Typing  the  rela,on  to  reflect   on  pragma-cs  of  use  of  tags   nt:TaggedResource   rdfs:Resource  nt:isRelatedTo   nt:TagAc7on(named  graph)  
  24. 24. 24 Tagging  model   Typing  the  named  graphs   for  addi-onal  dimensions   of  tagging   nt:TaggedResource   rdfs:Resource  nt:isRelatedTo   nt:TagAc7on(named  graph)  
  25. 25. 25 Tagging  model   Example  of  a  tagging  in  delicious   hZp://www.windenergy.com   nt:ManualTagAc7on   nt:isAbout   scot:Tag   #wind-­‐energy   <nt:TaggedResource  rdf:about="http://www.windenergy.com"        cos:graph="http://mysocialsi.te/tagaction#7182904">          <nt:isAbout  rdf:resource="http://mysocialsi.te/tag#wind-­‐energy"  />   </nt:TaggedResource>   freddy   sioc:has_creator   using  RDF  source  declara-on   delicious.com   sioc:has_container   <nt:ManualTagAction  rdf:about="http://mysocialsi.te/tagaction#7182904">    <sioc:has_creator  rdf:resource="http://mysocialsi.te/user#freddy"     </nt:ManualTagAction>  
  26. 26. 26 Folksonomy  enrichment   2  complementary  seman7c  enrichment:   hZp://www.windenergy.com   nt:ManualTagAc7on   nt:isAbout   wind-­‐energy   renewable     energy   windenergy   wind  turbine   has  broader   close  match   has  narrower   environment   related   Structuring tags as in a thesaurus (SKOS)
  27. 27. 27 Folksonomy  enrichment   2  complementary  seman7c  enrichment:   wind-­‐energy   renewable     energy   windenergy   wind  turbine   has  broader   close  match   has  narrower   environment   related   Structuring tags as in a thesaurus (SKOS)
  28. 28. 28 Folksonomy  enrichment   2  complementary  seman7c  enrichment:   wind-­‐energy   renewable     energy   windenergy   wind  turbine   has  broader   close  match   has  narrower   environment   related   Structuring tags as in a thesaurus (SKOS)
  29. 29. 29 Tagging  model   Suppor,ng  diverging  points  of  view   car   pollu7on  skos:related   john   agrees   paul   disagrees  
  30. 30. Suppor-ng  diverging  points  of  view   Reifica-on  of  rela7ons  with  named  graphs   30
  31. 31. Suppor-ng  diverging  points  of  view   Extending  SIOC  to  model  different  types  of  agents   31
  32. 32. Suppor-ng  diverging  points  of  view   Reifica-on  of  rela7ons  with  named  graphs   car   pollu7on  skos:related   srtag:SingleUser   "john"   srtag:hasApproved   srtag:SingleUser   "paul"   srtag:hasRejected   srtag:TagSeman7cStatement   srtag:TagStructureComputer   "r2d2"   srtag:hasProposed   32
  33. 33. 33 Ademe  scenario     Experts   produce  docs     +  tag   Archivists   centralize  +  tag   Public  audience   read  +  tag   Life-­‐cycle  grounded  on  usage  analysis  
  34. 34. 34 Ademe’s  dataset   Delicious TheseNet Cadic What Bookmarks of users of tag "ademe" Keywords for Ademe's PhD projects Archivists indexing lexicon # tags 1015 6583 1439 # resources 196 1425 4675 # tagging (1R - 1T - 1U) 3015 10160 25515 # users 812 1425 1
  35. 35. 4.  Going  through  the   folksonomy  enrichment   life-­‐cycle   35
  36. 36. ADDING TAGS Automatic processing User-centric structuring Detect conflicts Global structuring Flat folksonomy Structured folksonomy Folksonomy  enrichment  life-­‐cycle   36
  37. 37. ADDING TAGS Automatic processing User-centric structuring Detect conflicts Global structuring Flat folksonomy Structured folksonomy Folksonomy  enrichment  life-­‐cycle   37
  38. 38. Automatic processing 1.  String-based 2.  Co-occurrence patterns 3.  User-based associations Flat folksonomy 38 3 methods to automatically extract tags semantics
  39. 39. 39 1.  String-­‐based  metrics   pollution Soil pollutions pollutantpollution => « pollution » related to « pollutant » => « pollution » broader than « soil pollutions »
  40. 40. •  Benchmark  of  30  different  string-­‐based  similarity   from    SimMetrics*  :  σ  (t1,t2)  ∊  [0,  1]   •  Reference  data  set  built  with  Ademe  experts   •  Which  metric  is  best  for  which  rela0on  at  what   threshold  ?   •  Informa7on-­‐retrieval  metrics        precision,  recall,  and  F1-­‐measure   40 1.  String-­‐based  metrics   * http://staffwww.dcs.shef.ac.uk/people/S.Chapman/simmetrics.html
  41. 41. 1.  String-­‐based  metrics   41 •  MongeElkan_Soundex  to  detect  seman,cally  linked  tags    (close  match  +  hyponym  +  related)      threshold  =  0.8   •  JaroWinkler  to  dis7nguish  closeMatch    threshold  =  0.9   •  asymmetry  of  MongeElkan_QGram  to  dis7nguish  hyponyms   •  σ  (t1,t2)  ≠  σ  (t2,t1)   •  δ  =  σ  (t1,t2)  -­‐  σ  (t2,t1)  >  0.4  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
  42. 42. Cas 1.  String-­‐based  metrics  1.  String-­‐based  metrics   Heuris-c  in  3  steps   seman-cally  linked  :  MongeElkan-­‐Soundex  σ1   IF  σ1(t1,t2)  >  0.8      closeMatch  :  JaroWinkler  σ2      IF    σ2  (t1,t2)  >  0.9          =>  t1  closeMatch  t2      hyponym  :    MongeElkan-­‐QGram  σ3      ELSE  IF    σ3  (t1,t2)  -­‐  σ3  (t2,t1)    >  0.4    =>  t1  has  narrower  t2      related  otherwise      ELSE              =>  t1  related  t2     42
  43. 43. Cas 1.  String-­‐based  metrics  1.  String-­‐based  metrics   Performances   !" !#$" !#%" !#&" !#'" !#(" !#)" !#*" +,-../01"234/305" 67,8079" 4-.35-:" !"#$%&%'()*)"#$+,,) ;4-</+/80"6-=4/+><" ?-<3.."6-=4/+><" 43
  44. 44. 1.  String-based metrics results !"#$%&'"()&$ !"#$*"&&'+)&$ !"#$#,)--.*/$0"&."*1$ !"#$&)-"1)($ 1.  String-­‐based  metrics   44results on full dataset              tags  from  experts                tags  from  archivsts   close  match  related   broader  
  45. 45. 45 2.  Co-­‐occurrence  pacerns   Example  of  folksonomy   cc
  46. 46. ecology energy wind turbine sustainability housing ecology 0 1 1 3 1 energy 1 0 2 4 3 wind turbine 1 2 0 1 1 sustainability 3 4 1 0 4 housing 1 3 1 4 0 IF σ > 0.85 => "energy" related "sustainability" €  vecology €  venergy  vwind turbine  vsustainability €  vhousing 2.  Co-­‐occurrence  pacerns   46 σ(energy,sustainability) = cos(  venergy,  vsustainability )
  47. 47. 47 2.  Co-­‐occurrence  pacerns   Cadic dataset
  48. 48. renewable  energy   wind-­‐energy      Alex      Delphine      Claire      Monique      Anne   ⇒   Hyponym  rela7ons  (broader/narrower):      «  renewable  energy  »  broader  than  «  wind-­‐energy  »   3.  User-­‐based  associa-on   48
  49. 49. 3.  User-­‐based     associa-on   THESENET dataset 49
  50. 50. Global  results  of  automa-c  processings   Total  with  3  automa7c  methods:  83027  rela-ons  for  9037  tags   –  68633  related   –  11254  hyponym   –  3193  spelling  variants   50
  51. 51. ADDING TAGS Automatic processing User-centric structuring Detect conflicts Global structuring Flat folksonomy Structured folksonomy Folksonomy  enrichment  life-­‐cycle   51 compu7ng  server
  52. 52. !"#$%&'"()&$ !"#$*"&&'+)&$ !"#$#,)--.*/$0"&."*1$ !"#$&)-"1)($ 52 ?   Computed  rela0ons  are  not  always  accurate    
  53. 53. ADDING TAGS Automatic processing User-centric structuring Detect conflicts Global structuring Flat folksonomy Structured folksonomy Folksonomy  enrichment  life-­‐cycle   53 Firefox  extension  SRTAgEditor
  54. 54. 54 Capturing  users's  contribu-ons     Embedding  structuring  tasks  within  everyday  ac0vity  (searching  e.g)  
  55. 55. 55 Capturing  users's  contribu-ons    
  56. 56. 56 Capturing  user's  point  of  view   John   srtag:hasRejected   energie   france   skos:broader   srtag:TagSeman7cStatement   Exemple:   Rejec7ng  a  rela7on  
  57. 57. 57 Capturing  user's  point  of  view   John   srtag:hasRejected   energie   energy   skos:related   srtag:TagSeman7cStatement   Exemple:   Proposing  another   rela7on   energie   energy   skos:closeMatch   srtag:TagSeman7cStatement   srtag:hasProposed  
  58. 58. 58 Capturing  user's  point  of  view   John   srtag:hasRejected   energie   energy   skos:related   srtag:TagSeman7cStatement   Exemple:   Proposing  another   rela7on   energie   energy   skos:closeMatch   srtag:TagSeman7cStatement   srtag:hasProposed  
  59. 59. 59 Capturing  user's  point  of  view   John   srtag:hasRejected   energie   energy   skos:related   srtag:TagSeman7cStatement   Exemple:   Proposing  another   rela7on   energie   energy   skos:closeMatch   srtag:TagSeman7cStatement   srtag:hasProposed  
  60. 60. ADDING TAGS Automatic processing User-centric structuring Detect conflicts Global structuring Flat folksonomy Structured folksonomy Folksonomy  enrichment  life-­‐cycle   60
  61. 61. 61 Conflict  detec-on   environment   pollu7on   Using rules: IF num(narrower)/num(broader) ≥ c THEN narrower wins ELSE related wins narrower John   srtag:hasApproved   Anne   srtag:hasApproved   broader Monique   srtag:hasApproved   Delphine   srtag:hasApproved  
  62. 62. 62 Conflict  detec-on   related broader narrower less constrained less constrained less constrained close match relatedenvironment   pollu7on   narrower broader
  63. 63. 63 Experimenta-on  at  ADEME   Par7cipa7on  of  3  members  at  Ademe     +  2  professionals  in  environment     Si je cherche des informations, je dois pouvoir utiliser indifféremment le Tag1 ou le Tag2 Si je cherche des informations liées à Tag1, les informations liées à Tag2 sont pertinentes, mais pas le contraire Si je cherche des informations liées à Tag2, les informations liées à Tag1 sont pertinentes, mais pas le contraire Si je cherche des informations sur l'un des tags, il est pertinent de suggérer des informations sur l'autre tag (Tag1 et Tag2 sont équivalents) (Tag1 est plus général que Tag2) (Tag2 est plus général que Tag1) (Tag1 et Tag2 liés) agriculture durable agriculture raisonnee biologie agriculture biologique changements sociaux changement social chimie verte chanvre Climat/changement changement climatique collectivite action collective collectivite collecte de donnees commande communication entre acteurs comportements pro- environnementaux comportements pro- environnemental compost composant conception ecoconception conception travail collaboratif vis a vis de la conception cycle de rankine cycle organique de rankine developpement durable developpement local accumulateurs li-ion tours d'habitation acteurs du territoire territorialite agglomeration cooperation agriculture durable agriculture biologique diversite culturelle diversite microbienne ecologie ecology elements finis methode des elements finis energie politique energetique energie production energie energie energie renouvelable energie autonomie energetique energy energies Nom Prénom : Poste : Profil en quelques mots-clés : Indiquer par un "X" la relation que vous jugez la plus exacte entre les deux tags. Choisissez une seule relation pour chaque tag. Les deux premières lignes sont des exemples fictifs. Tag1 Tag2 Ces 2 tags ne sont pas spécialement liés
  64. 64.  Several  cases  of  conflic-ng  situa-ons   Conflic-ng  :  >1  rela7on   per  pair  of  tags   Approved  :  1  rela7on,   only  approved   Debatable  :  1  rela7on,   BOTH  approved  and   rejected   Rejected  :  1  rela7on,  only   rejected   !"#$%&'#() *+,) -../"012) 34,) 516787691) :;,) <1=1&812) :+,) !"#$%&'("&$)*+,&-$'.$/'012/-$+'&3204$ 64
  65. 65.  Several  cases  of  conflic-ng  situa-ons   Distribu-on  over     rela-on  types  :   •   "closeMatch"  tends   to  draw  a  consensus   more  easily  than   others   •   "broader/ narrower"    and   "related"  cause  more   debates/conflicts   !"# $!"# %!"# &!"# '!"# (!"# )!"# *!"# +!"# ,!"# $!!"# -./01234-5# 67/3817# 9377/:17# 71.3418# !"#$%&'()&"*+,-$,.$/,-0&/($/1'2'$,32)$)241+,-$(562'$ ;/9<=-4#0/.>17#?7/?/03.# @??7/>18# A163418# B1C1-418# 65
  66. 66.  Several  cases  of  conflic-ng  situa-ons   Influence  of     compound  words   ? !"# $!"# %!"# &!"# '!"# (!"# )!"# *!"# +!"# ,!"# $!!"# -./0.12345.637# 08967# :.24;./0.123# 5.637#08967# <==#08967# -.2>9;?2@# <006.AB3# CBD8E8D=B# FBGB;EB3# energy   renewable   energy   80%   46%   66
  67. 67. Example  conflict  resolu7on   Conflic7ng   Conflict  solver  choice   debatable   rejected   67
  68. 68. ADDING TAGS Automatic processing User-centric structuring Detect conflicts Global structuring Flat folksonomy Structured folksonomy Folksonomy  enrichment  life-­‐cycle   68
  69. 69. Helping  Referent  User  (Ademe  archivists)  choose  solu0ons  to   conflicts   Repor-ng   69
  70. 70. 70 Global  map   Includes  all  points  of  view,  highlights  conflicts  +  consensuses  
  71. 71. Referent  choices   71 Choices  of  the  referent  user  (archivists  at  Ademe  e.g.)  
  72. 72. Referent  choices   72
  73. 73. ADDING TAGS Automatic processing User-centric structuring Detect conflicts Global structuring Flat folksonomy Structured folksonomy Folksonomy  enrichment  life-­‐cycle   73
  74. 74. Enriching  individual  points  of  view   Integra7ng  others'  contribu7ons:   1.  Current  user  -­‐>  "Anne"   2.  ReferentUser  (e.g.  archivists)   3.  ConflictSolver  (sowware  agent)   4.  Other  individual  users   5.  Automatons  (metrics)   BROADER   NARROWER   RELATED   CLOSE  MATCH   environnement  Search:   preoccupa7on  environnementales   grenelle  de  l  environnement   competences  environnementales   environment   environmental   domaines  environnementaux   Anne  is  looking  for  tag   "environnement"   74
  75. 75. Each    point  of  view   corresponds  to  a  layer   75
  76. 76. 5.  Conclusion   76
  77. 77. 77 What  we  do  :   Help  online  communi7es                                         structure  their  tags  wind-­‐energy   renewable     energy   sustainability   wind  turbine   has  broader   related   has  narrower   environment   related  
  78. 78.   An  approach  to  bridge    tagging  with  Seman-c  Web:       NiceTag  for  tagging       SRTag  for  mul7-­‐points  of  view  structuring  of  tags     Complete  life-­‐cycle  of  folksonomy  enrichment     Automa-c  processing  of  tags:     String-­‐based  heuris-c     State  of  the  art  methods  integrated  in  Seman7c  Web   compu7ng  environment  (Corese  Sparql  engine)     User  interface  to  capture  tag  structuring  embedded  in   every-­‐day  tasks     Implementa-on  within  ISICIL  solu7on  (tagging  server)   78 Our  contribu-ons:  
  79. 79. •  More  user  interfaces  :   •  Collabora-ve  aspects   •  Visualisa-on  of  large  structured  folksonomy   •  Tag  searching     •  Other  computa7onal  methods  +  op7miza7on   •  ISICIL  :  test  with  final  users  Ademe  and  Orange  labs   •  Tes7ng  on  other  types  of  communi7es  (Life2Times)   •  Temporal  dimension   •  Mul7linguism   •  Integra7ng  collabora-ve  ergonomics  in  design  processes   79 Future  work  
  80. 80. 80 Thank  you  !   freddy.limpens@inria.fr   hZp://www-­‐sop.inria.fr/members/Freddy.Limpens/  
  81. 81. 2010   •  Monnin,  A.;  Limpens,  F.;  Gandon,  F.  &  Laniado,  D.  Speech  acts  meets  tagging:  NiceTag  ontology  AIS  SigPrag  Interna7onal  Pragma7c  Web   Conference,  2010   •  Monnin,  A.;  Limpens,  F.;  Gandon,  F.  &  Laniado,  D.  ,L'ontologie  NiceTag  :  les  tags  en  tant  que  graphes  nommés,A.  Monnin,  F.  Limpens,  D.   Laniado,  F.  Gandon,  EGC  2010,  Atelier  Web  Social   •  Limpens,  F.;  Gandon,  F.  &  Buffa,  M.  Helping  online  communi-es  to  seman-cally  enrich  folksonomies  Proceedings  of  the  WebSci10:   Extending  the  Fron7ers  of  Society  On-­‐Line,  hZp://webscience.org,  2010   2009   •  Limpens,  F.;  Monnin,  A.;  Laniado,  D.  &  Gandon,  F.  NiceTag  Ontology:  tags  as  named  graphs  Interna7onal  Workshop  in  Social  Networks   Interoperability,  ASWC09,  2009   •  Limpens,  F.;  Gandon,  F.  &  Buffa,  M.  Séman-que  des  folksonomies  :  structura-on  collabora-ve  et  assistée  Ingénierie  des  Connaissances,   2009     •  Limpens,  F.;  Gandon,  F.  &  Buffa,  M.  Collabora-ve  seman-c  structuring  of  folksonomies  (short  ar-cle)  IEEE/WIC/ACM  Int.  Conf.  on  Web   Intelligence,  2009   •  Erétéo,  G.;  Buffa,  M.;  Gandon,  F.;  Leitzelman,  M.  &  Limpens,  F.  Leveraging  Social  data  with  Seman-cs  W3C  Workshop  on  the  Future  of   Social  Networking,  Barcelona.,  2009   •  Henri,  F.;  Charlier,  B.  &  Limpens,  F.  Understanding  and  Suppor-ng  the  Crea-on  of  More  Effec-ve  PLE  Int.  Conf.  on  Informa7on  Resources   Management,  Dubai,  2009   2008     •  Henri,  F.;  Charlier,  B.  &  Limpens,  F.  Understanding  PLE  as  an  Essen-al  Component  of  the  Learning  Process  World  Conf.  on  Educa7onal   Mul7media,  Hypermedia  &  Telecommunica7ons,  ED-­‐Media,  Vienna,  Austria,  2008     •  Limpens,  F.;  Gandon,  F.  &  Buffa,  M.  Rapprocher  les  ontologies  et  les  folksonomies  pour  la  ges-on  des  connaissances  partagées  :  un  Etat   de  l'art  Proc.  19èmes  journées  francophones  d'Ingénierie  des  Connaissances,  Nancy,  2008   •  Limpens,  F.;  Gandon,  F.  &  Buffa,  M.  Bridging  Ontologies  and  Folksonomies  to  Leverage  Knowledge  Sharing  on  the  Social  Web:  a  Brief   Survey  Proc.  1st  Interna7onal  Workshop  on  Social  Sowware  Engineering  and  Applica7ons  (SoSEA),     http://www-­‐sop.inria.fr/members/Freddy.Limpens/?q=biblio   81 Personal  publica-ons  
  82. 82. ANGELETOU  S.,  SABOU  M.  &  MOTTA  E.  (2008).  Seman7cally  Enriching  Folksonomies  with  FLOR.  In  CISWeb  Workshop  at   European  Seman7c  Web  Conference  ESWC.   BRAUN  S.,  SCHMIDT  A.,  WALTER  A.,  NAGYPÁL  G.  &  ZACHARIAS  V.  (2007).  Ontology  maturing:  a  collabora7ve  web  2.0   approach  to  ontology  engineering.  In  CKC,  volume  273  of  CEUR  Workshop  Proceedings:  CEURWS.org.   CATTUTO  C.,  BENZ  D.,  HOTHO  A.  &  STUMME  G.  (2008).  Seman7c  grounding  of  tag  relatedness  in  social  bookmarking   systems.  In  Proceedings  of  the  7th  Interna7onal  Conference  on  The  Seman7c  Web,  Berlin,  Heidelberg:  Springer-­‐ Verlag.   GANDONF.,BOTTOLIERV.,CORBYO.&DURVILLEP.  (2007).Rdf/xml  source  declara7on,  w3c  member  submission.  hZp:// www.w3.org/Submission/rdfsource/.   HALPIN  H.  &  PRESUTTI  V.  (2009).  An  ontology  of  resources:  Solving  the  iden7ty  crisis  in  ESWC,  volume  5554  of  Lecture   Notes  in  Computer  Science,  p.  521–534:  Springer.   HOTHO  A.,  JÄSCHKE  R.,  SCHMITZ  C.  &  STUMME  G.  (2006).  Informa7on  retrieval  in  folksonomies:  Search  and  ranking.  In   The  Seman7c  Web:  Research  and  Applica-­‐  7ons,  LNCS(4011)  ,  Heidelberg:  Springer.   HUYNH-­‐KIM  BANG  B.,  DANÉ  E.  &  GRANDBASTIEN  M.  (2008).  Merging  seman7c  and  par7cipa7ve  approaches  for   organizing  teachers’  documents.  In  Proceedings  of  World  Conference  on  Educa7onal  Mul7media,  Hypermedia  &   Telecommunica7ons,  p.  x4959–4966,  Vienna  France.   KIM  H.-­‐L.,  YANG  S.-­‐K.,  SONG  S.-­‐J.,  BRESLIN  J.  G.  &  KIM  H.-­‐G.  (2007).  Tag  Mediated  Society  with  SCOT  Ontology.  In  Seman7c   Web  Challenge,  ISWC.   LIN  H.  &  DAVIS  J.  (2010).  Computa7onal  and  crowdsourcing  methods  for  extrac7ng  ontological  structure  from   folksonomy.  In  ESWC  (2),  volume  6089  of  Lecture  Notes  in  Computer  Science,  p.  472–477:  Springer.   MIKA  P.  (2005).  Ontologies  are  Us:  a  Unified  Model  of  Social  Networks  and  Seman7cs.  In  ISWC,  volume  3729  of  LNCS,  p.   522–536:  Springer.   MONNIN  A.,  LIMPENS  F.,  GANDON  F.  &  LANIADO  D.  (2010).  Speech  acts  meet  tagging:  Nicetag  ontology.  In  I-­‐SEMANTICS   ’10:  Proceedings  of  the  6th  Interna7onal  Conference  on  Seman7c  Systems,  p.  1–10,  New  York,  NY,  USA:  ACM.   PASSANT  A.  &  LAUBLET  P.  (2008).  Meaning  of  a  tag:  A  collabora7ve  approach  to  bridge  the  gap  between  tagging  and   linked  data.  In  Proceedings  of  the  WWW  2008  Workshop  Linked  Data  on  the  Web  (LDOW2008),  Beijing,  China.   SPECIA  L.  &  MOTTA  E.  (2007).  Integra7ng  folksonomies  with  the  seman7c  web.  In  Proc.  of  the  European  Seman7c  Web   Conference  (ESWC2007),  volume  4519  of  LNCS,  p.  624–639,  Berlin  Heidelberg,  Germany:  Springer-­‐Verlag.   82 References  

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