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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 ...

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

  • Multi-points of 
 view semantic 
 enrichment of folksonomies" Freddy Limpens Edelweiss, INRIA Sophia Antipolis Picasso  129ieth  birthday   Supervisors Fabien Gandon, Edelweiss, INRIA Sophia Antipolis Michel Buffa, Kewi/I3S, UNSA/CNRS Edelweiss   Ph.D Thesis defense – October 25th 2010 1
  • 1.  Context  and   mo-va-ons   2
  • Context  of  the  thesis   •  Online  communi7es  of  interest   •  "Enterprise  2.0"  &  organiza7ons   ⇒ Cross-­‐fer7lizing  Web  2.0  and   Seman7c  Web   3
  • Context  of  the  thesis   •  Tools  for  techno/science  monitoring   •  Experts  seeking   •  Industrial  partners:   •  Academic  partners:     4
  • From  social  tagging  to  folksonomies   Tags  freely  associated  to  resources  …     …  collected  and  shared  on  the  web   5
  • …  resul7ng  in   FO LKSO NO M IES   A  mass  of  users  for  a  mass  of  resources   6
  • Limita-ons  of  folksonomies   Spelling  varia-ons  of  tags:   newyork  =  new_york    =  nyc     7
  • Limita-ons  of  folksonomies   Ambiguity  of  tags   paris   …  in  France  ?   …  or  in    Texas,  USA  ?   8
  • Limita-ons  of  folksonomies   Lack  of  seman-c   links  between     tags   9
  • How  to  turn     folksonomies  ...   ? pollutant related Energy related pollution ...  into   has narrower  topic  structures  (thesaurus)  ?   Soil pollutions 10
  • …  without  overloading  users   … and by collecting all user's expertise into the process 11
  • Outline  of  the  presenta-on   1. Context  and  mo7va7ons   2. State  of  the  art  and  posi7oning   3. Tagging  &  folksonomy  enrichment   models   4. Folksonomy  enrichment  life-­‐cycle   12
  • 2.   State  of  the  art   and  posi-oning   13
  • 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)     pollutant Energy related related pollution has narrower Soil pollutions 14
  • a relation, depending on the actual context. This fact We now demonstrate some functionality of the tool in is acknowledged by many ontology formalisms that al- terms of the steps of our development methodology. low metamodeling. Using imagenotions, users do not State  of  the  art   need to understand this somewhat artificial separation of notions. 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 2. Because imagenotions are associated with images, they elephants. The imagenotion “elephant” was so far not avail- are meaningful internationally as an image has the able. Therefore, she creates a new imagenotion, adds an same meaning in different languages. image or part of an image that shows elephants and starts Involving  users  iontology of imagenotions.guide the process of n  tags  structuring:   of The goal of our methodology is to creating an The main steps describing the new imagenotion with more details. She uses English as spoken language. As synonyms, she enters “ele- this methodology is based on the ontology maturing process phantidae” and “tusker”. Instead of tagging the new images model: that show elephants with these words, she can use the new •  Simple  syntax  to  structure  step, new (imagenotions are 1. Emergence of Ideas. In this tags   Huyn-­‐Kim   imagenotion—she just pulls this imagenotion over the new images via drag and drop. Bang  et  al.  2008)   can jointlythis step can become collaborative, created. Already as users collect the tags describing imageno- tions, and select the most representative images for an •  Crowdsourcing  strategy  to  validate  tag-­‐ imagenotion. Collaborative editing is especially use- ful in a multi-lingual environment where it cannot be concepts  mapping  (Lin  et  al.  2010)   expected that any individual user speaks all required languages. •  Integrate  ontology  imagenotions, it cannot be avoided that for create new maturing  into  Social   2. Consolidation in Communities. Because it is so easy to Bookmarking  tool  (Braun  et  aalso2in different languages) or are created (synonyms, l.   007)   the same semantic notion initially many imagenotions 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 Figure 2: Editing an imagenotion with the No- than one notion. tionEditor tool pollutant Energy related related pollution has narrower Soil pollutions 15
  • State  of  the  art   Tags  and  Seman-c  Web  models   •  SCOT  for  tags  and  tagging  (Kim  et  al.  2007):   16
  • 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   17
  • Posi-oning   Computed   Tag-­‐Concept   Users'   Sem-­‐Web   Mul7-­‐points   Tag  similarity   mapping   contrib.   formalism   of  view   Angeletou  et  al.   ✓   ✓   ✓   (2008)   Huynh-­‐Kim  Bang   ✓   ✓   et  al.  (2008)   Passant  &  Laublet ✓   ✓   ✓   (2008)   Lin  &  Davis   ✓   ✓   ✓   ✓   (2010)   Braun  et  al.   ✓   ✓   (2007)   Our  approach   ✓   ✓   ✓   ✓   18
  • 3.  Tagging  &  folksonomy   enrichment  models   19
  • Tagging  model   What  is  a  tagging  ?   "nature"! (1)   (2)   (3)   picture   shows   "nature"   place   located   l:england   edi7ng   makes  me   :  )   Tagging  =  linking  a  resource  with  a  sign   20
  • Tagging  model   NiceTag  (Monnin  et  al,  2010):          Tagging  as  named  graphs*   nt:TagAc7on(named  graph)   nt:TaggedResource   nt:isRelatedTo   rdfs:Resource   dc:date   sioc:has_creator   sioc:has_container   xsd:Date   sioc:UserAccount   sioc:Container   *Carrol  et  al.  (2005) 21
  • Tagging  model   nt:TagAc7on(named  graph)   No  constraints  on  the  model   nt:TaggedResource   nt:isRelatedTo   rdfs:Resource   of  the  sign  used  to  tag   moat:Tag   moat:hasMeaning   nt:isRelatedTo   hZp:geonames.org/2990440   nt:isRelatedTo   :)   nt:TaggedResource   nt:isRelatedTo   scot:Tag   nt:isRelatedTo   skos:Concept   nt:isRelatedTo   22
  • Tagging  model   nt:TagAc7on(named  graph)   Typing  the  rela,on  to  reflect   on  pragma-cs  of  use  of  tags   nt:TaggedResource   nt:isRelatedTo   rdfs:Resource   23
  • Tagging  model   nt:TagAc7on(named  graph)   Typing  the  named  graphs   for  addi-onal  dimensions   nt:TaggedResource   nt:isRelatedTo   rdfs:Resource   of  tagging   24
  • Tagging  model   Example  of  a  tagging  in  delicious   nt:ManualTagAc7on   scot:Tag   hZp://www.windenergy.com   nt:isAbout   #wind-­‐energy   sioc:has_creator   sioc:has_container   freddy   delicious.com   using  RDF  source  declara-on   <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>   <nt:ManualTagAction  rdf:about="http://mysocialsi.te/tagaction#7182904">    <sioc:has_creator  rdf:resource="http://mysocialsi.te/user#freddy"     </nt:ManualTagAction>   25
  • Folksonomy  enrichment   2  complementary  seman7c  enrichment:   environment   renewable     energy   related   nt:ManualTagAc7on   has  broader   hZp://www.windenergy.com   nt:isAbout   wind-­‐energy   close  match   has  narrower   windenergy   wind  turbine   Structuring tags as in a thesaurus (SKOS) 26
  • Folksonomy  enrichment   2  complementary  seman7c  enrichment:   environment   renewable     energy   related   has  broader   wind-­‐energy   close  match   has  narrower   windenergy   wind  turbine   Structuring tags as in a thesaurus (SKOS) 27
  • Folksonomy  enrichment   2  complementary  seman7c  enrichment:   environment   renewable     energy   related   has  broader   wind-­‐energy   close  match   has  narrower   windenergy   wind  turbine   Structuring tags as in a thesaurus (SKOS) 28
  • Tagging  model   Suppor,ng  diverging  points  of  view   car   skos:related   pollu7on   agrees   disagrees   john   paul   29
  • Suppor-ng  diverging  points  of  view   Reifica-on  of  rela7ons  with  named  graphs   30
  • Suppor-ng  diverging  points  of  view   Extending  SIOC  to  model  different  types  of  agents   31
  • Suppor-ng  diverging  points  of  view   Reifica-on  of  rela7ons  with  named  graphs   srtag:TagSeman7cStatement   car   skos:related   pollu7on   srtag:hasProposed   srtag:hasApproved   srtag:hasRejected   srtag:TagStructureComputer   srtag:SingleUser   srtag:SingleUser   "r2d2"   "john"   "paul"   32
  • Ademe  scenario     Life-­‐cycle  grounded  on  usage  analysis   Public  audience   Experts   read  +  tag   produce  docs     Archivists   centralize  +  tag   +  tag   33
  • Ademe’s  dataset   Delicious TheseNet Cadic Bookmarks of Keywords for Archivists What users of tag Ademe's PhD indexing lexicon "ademe" projects # tags 1015 6583 1439 # resources 196 1425 4675 # tagging 3015 10160 25515 (1R - 1T - 1U) # users 812 1425 1 34
  • 4.  Going  through  the   folksonomy  enrichment   life-­‐cycle   35
  • Folksonomy  enrichment  life-­‐cycle   User-centric structuring Flat Automatic folksonomy processing Detect ADDING TAGS conflicts Structured Global folksonomy structuring 36
  • Folksonomy  enrichment  life-­‐cycle   User-centric structuring Flat Automatic folksonomy processing Detect ADDING TAGS conflicts Structured Global folksonomy structuring 37
  • 3 methods to automatically extract tags semantics Flat Automatic processing folksonomy 1.  String-based 2.  Co-occurrence patterns 3.  User-based associations 38
  • 1.  String-­‐based  metrics   pollution Soil pollutions => « pollution » broader than « soil pollutions » pollution pollutant => « pollution » related to « pollutant » 39
  • 1.  String-­‐based  metrics   •  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   * http://staffwww.dcs.shef.ac.uk/people/S.Chapman/simmetrics.html 40
  • 1.  String-­‐based  metrics   •  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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
  • 1.  String-­‐based  metrics   Cas in  3  steps   Heuris-c   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
  • 1.  String-­‐based  metrics   Cas Performances   !#*" ;4-</+/80"6-=4/+><" ?-<3.."6-=4/+><" !#)" !#(" !#'" !"#$%&%'()*)"#$+,,) !#&" !#%" !#$" !" +,-../01"234/305" 67,8079" 4-.35-:" 43
  • 1.  String-based 1.  String-­‐based  metrics   metrics results              tags  from  experts                tags  from  archivsts   related   close  match   broader   !"#$%&'"()&$ !"#$*"&&'+)&$ !"#$#,)--.*/$0"&."*1$ !"#$&)-"1)($ results on full dataset 44
  • 2.  Co-­‐occurrence  pacerns   Example  of  folksonomy   c 45
  • 2.  Co-­‐occurrence  pacerns   ecology energy wind turbine sustainability housing  v ecology ecology 0 1 1 3 1  v energy energy 1 0 2 4 3  € v wind turbine wind turbine 1 2 0 1 1  € v sustainability sustainability 3 4 1 0 4  v housing housing 1 3 1 4 0   σ (energy,sustainability) = cos(v energy , v sustainability ) € IF σ > 0.85 => "energy" related "sustainability" 46
  • 2.  Co-­‐occurrence  pacerns   Cadic dataset 47
  • 3.  User-­‐based  associa-on   renewable  energy   wind-­‐energy      Claire      Alex      Anne      Delphine      Monique   ⇒   Hyponym  rela7ons  (broader/narrower):      «  renewable  energy  »  broader  than  «  wind-­‐energy  »   48
  • 3.  User-­‐based     associa-on   THESENET dataset 49
  • 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
  • Folksonomy  enrichment  life-­‐cycle   compu7ng  server User-centric structuring Flat Automatic folksonomy processing Detect ADDING TAGS conflicts Structured Global folksonomy structuring 51
  • Computed  rela0ons  are  not  always  accurate     ?   !"#$%&'"()&$ !"#$*"&&'+)&$ !"#$#,)--.*/$0"&."*1$ !"#$&)-"1)($ 52
  • Folksonomy  enrichment  life-­‐cycle   Firefox  extension  SRTAgEditor User-centric structuring Flat Automatic folksonomy processing Detect ADDING TAGS conflicts Structured Global folksonomy structuring 53
  • Capturing  users's  contribu-ons     Embedding  structuring  tasks  within  everyday  ac0vity  (searching  e.g)   54
  • Capturing  users's  contribu-ons     55
  • Capturing  user's  point  of  view   Exemple:   Rejec7ng  a  rela7on   srtag:TagSeman7cStatement   John   energie   srtag:hasRejected   skos:broader   france   56
  • Capturing  user's  point  of  view   Exemple:   Proposing  another   rela7on   John   srtag:TagSeman7cStatement   srtag:TagSeman7cStatement   srtag:hasRejected   energie   srtag:hasProposed   energie   skos:related   skos:closeMatch   energy   energy   57
  • Capturing  user's  point  of  view   Exemple:   Proposing  another   rela7on   John   srtag:TagSeman7cStatement   srtag:TagSeman7cStatement   srtag:hasRejected   energie   srtag:hasProposed   energie   skos:related   skos:closeMatch   energy   energy   58
  • Capturing  user's  point  of  view   Exemple:   Proposing  another   rela7on   John   srtag:TagSeman7cStatement   srtag:TagSeman7cStatement   srtag:hasRejected   energie   srtag:hasProposed   energie   skos:related   skos:closeMatch   energy   energy   59
  • Folksonomy  enrichment  life-­‐cycle   User-centric structuring Flat Automatic folksonomy processing Detect ADDING TAGS conflicts Structured Global folksonomy structuring 60
  • Conflict  detec-on   John   Anne   srtag:hasApproved   srtag:hasApproved   narrower environment   pollu7on   broader srtag:hasApproved   srtag:hasApproved   Monique   Delphine   Using rules: IF num(narrower)/num(broader) ≥ c THEN narrower wins ELSE related wins 61
  • Conflict  detec-on   narrower environment   related pollu7on   broader related less constrained less constrained less constrained broader narrower close match 62
  • Nom Prénom : Experimenta-on  at  ADEME   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. Si je cherche des Si je cherche des Si je cherche des Si je cherche des informations liées à informations liées à informations sur l'un informations, je dois Tag1, les informations Tag2, les informations des tags, il est Ces 2 tags ne pouvoir utiliser liées à Tag2 sont liées à Tag1 sont pertinent de suggérer sont pas Tag1 Tag2 indifféremment le pertinentes, mais pas pertinentes, mais pas des informations sur spécialement Tag1 ou le Tag2 le contraire le contraire l'autre tag liés (Tag1 et Tag2 sont (Tag1 est plus général (Tag2 est plus général (Tag1 et Tag2 liés) équivalents) que Tag2) que Tag1) 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- comportements pro- environnementaux environnemental compost composant conception ecoconception travail collaboratif vis a vis de la conception 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 Par7cipa7on  of  3  members  at  Ademe     diversite culturelle ecologie diversite microbienne ecology +  2  professionals  in  environment     elements finis methode des elements finis energie politique energetique energie production energie energie energie renouvelable energie autonomie energetique 63 energy energies
  •  Several  cases  of  conflic-ng  situa-ons   <1=1&812) Conflic-ng  :  >1  rela7on   :+,) per  pair  of  tags   Approved  :  1  rela7on,   516787691) !"#$%&'#() only  approved   :;,) *+,) Debatable  :  1  rela7on,   BOTH  approved  and   rejected   -../"012) Rejected  :  1  rela7on,  only   34,) rejected   !"#$%&'("&$)*+,&-$'.$/'012/-$+'&3204$ 64
  •  Several  cases  of  conflic-ng  situa-ons   ;/9<=-4#0/.>17#?7/?/03.# @??7/>18# A163418# B1C1-418# $!!"# Distribu-on  over     ,!"# rela-on  types  :   +!"# •   "closeMatch"  tends   *!"# to  draw  a  consensus   )!"# more  easily  than   others   (!"# •   "broader/ '!"# narrower"    and   &!"# "related"  cause  more   %!"# debates/conflicts   $!"# !"# -./01234-5# 67/3817# 9377/:17# 71.3418# 65 !"#$%&'()&"*+,-$,.$/,-0&/($/1'2'$,32)$)241+,-$(562'$
  •  Several  cases  of  conflic-ng  situa-ons   -.2>9;?2@# <006.AB3# CBD8E8D=B# FBGB;EB3# $!!"# Influence  of     80%   ,!"# compound  words   +!"# *!"# energy   )!"# (!"# 46%   ? '!"# &!"# renewable   %!"# energy   $!"# !"# -./0.12345.637# :.24;./0.123# <==#08967# 08967# 5.637#08967# 66
  • Conflic7ng   Example  conflict  resolu7on   Conflict  solver  choice   debatable   rejected   67
  • Folksonomy  enrichment  life-­‐cycle   User-centric structuring Flat Automatic folksonomy processing Detect ADDING TAGS conflicts Structured Global folksonomy structuring 68
  • Repor-ng   Helping  Referent  User  (Ademe  archivists)  choose  solu0ons  to   conflicts   69
  • Global  map   Includes  all  points  of  view,  highlights  conflicts  +  consensuses   70
  • Referent  choices   Choices  of  the  referent  user  (archivists  at  Ademe  e.g.)   71
  • Referent  choices   72
  • Folksonomy  enrichment  life-­‐cycle   User-centric structuring Flat Automatic folksonomy processing Detect ADDING TAGS conflicts Structured Global folksonomy structuring 73
  • Enriching  individual  points  of  view   Integra7ng  others'  contribu7ons:   Anne  is  looking  for  tag   "environnement"   1.  Current  user  -­‐>  "Anne"   2.  ReferentUser  (e.g.  archivists)   3.  ConflictSolver  (sowware  agent)   4.  Other  individual  users   5.  Automatons  (metrics)   domaines  environnementaux   BROADER   RELATED   Search:   environnement   NARROWER   CLOSE  MATCH   preoccupa7on  environnementales   environmental   grenelle  de  l  environnement   environment   competences  environnementales   74
  • Each    point  of  view   corresponds  to  a  layer   75
  • 5.  Conclusion   76
  • What  we  do  :   Help  online  communi7es                                         environment   renewable     energy   related   has  broader   wind-­‐energy   structure  their  tags   related   has  narrower   sustainability   wind  turbine   77
  • Our  contribu-ons:     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
  • Future  work   •  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
  • Thank  you  !   freddy.limpens@inria.fr   hZp://www-­‐sop.inria.fr/members/Freddy.Limpens/   80
  • Personal  publica-ons   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
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