Bridging Social Web and Sem Web : 2 application cases in the field of sustainable developpment
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This document discusses bridging collaborative tagging systems and semantic web technologies. It describes how folksonomies generated through social tagging have limitations like lack of semantics and standardization. Methods are proposed to structure folksonomies by involving users and using automatic processing to extract tag semantics. An example scenario of managing an academic document corpus with folksonomies is provided. The document also outlines a lifecycle approach to enrich folksonomies by adding user-generated structure and detecting conflicts before achieving a global structured folksonomy.
Bridging Social Web and Sem Web : 2 application cases in the field of sustainable developpment
- 1. From folksonomies to structured knowledge representations: bridging Collaborative Web and Semantic Web Des folksonomies aux repr辿sentations structur辿es de connaissances: faire le pont entre Web Collaboratif et Web S辿mantique Freddy Limpens fdy@pl-足area.net h0p://pl-足area.net A D B S / C o o p 辿 r a - o n & D 辿 v e l o p p e m e n t A t e l i e r W e b S 辿 m a n - q u e e t D 辿 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. From social tagging to folksonomies Tags freely associated to resources collected and shared on the web 2
- 3. resul=ng in FO LKSO NO M IES A mass of users for a mass of resources 3
- 4. Limita-ons of folksonomies Spelling varia-ons of tags: newyork = new_york = nyc 4
- 5. Limita-ons of folksonomies Lack of seman-c links between tags 5
- 6. Limita-ons of folksonomies Lack of interoperability between social data repositories 6
- 7. How to turn folksonomies ... ? pollutant related Energy related pollution ... into has narrower topic structures (thesaurus) ? Soil pollutions 7
- 8. 1. State of the art 8
- 9. State of the art Involving users in tags structuring: ≒ Simple syntax to structure tags (Huyn-足Kim Bang et al. 2008) ≒ Crowdsourcing strategy to validate tag-足 concepts mapping (Lin et al. 2010) pollutant Energy related related pollution has narrower Soil pollutions 9
- 10. State of the art Automa-c extrac-on of tag seman-cs: Energy pollutant related related pollution has narrower Soil pollutions 10
- 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-足energy h0p://www.windenergy.com 11
- 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. 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-足energy sioc:has_creator sioc:has_container sioc:UserAccount sioc:Container freddy delicious.com *Carrol et al. (2005) 13
- 14. Tags and Seman-c Web models 2 complementary seman=c enrichment: environment renewable energy related has broader wind-足energy close match has narrower windenergy wind turbine Structuring tags as in a thesaurus (SKOS) 14
- 15. 2. 1st Applica-on case : Corpus management at ADEME 15
- 16. Ademe scenario Public audience Experts read + tag produce docs Archivists centralize + tag + tag 16
- 17. Folksonomy enrichment life-足cycle User-centric structuring Flat Automatic folksonomy processing Detect ADDING TAGS conflicts Structured Global folksonomy structuring 17
- 18. Folksonomy enrichment life-足cycle User-centric structuring Flat Automatic folksonomy processing Detect ADDING TAGS conflicts Structured Global folksonomy structuring 18
- 19. 1. String-足based metrics pollution Soil pollutions => 束 pollution 損 broader than 束 soil pollutions 損 pollution pollutant => 束 pollution 損 related to 束 pollutant 損 19
- 20. 3. User-足based associa-on renewable energy wind-足energy Claire Alex Anne Delphine Monique Hyponym rela=ons (broader/narrower): 束 renewable energy 損 broader than 束 wind-足energy 損 20
- 21. 3. User-足based associa-on 21
- 22. Computed rela.ons are not always accurate %&'"()&$ *"&&'+)&$ #,)--.*/$0"&."*1$ &)-"1)($ 22
- 23. Folksonomy enrichment life-足cycle User-centric structuring Flat Automatic folksonomy processing Detect ADDING TAGS conflicts Structured Global folksonomy structuring 23
- 24. Capturing users's contribu-ons Embedding structuring tasks within everyday ac.vity (searching e.g) 24
- 26. Folksonomy enrichment life-足cycle User-centric structuring Flat Automatic folksonomy processing Detect ADDING TAGS conflicts Structured Global folksonomy structuring 26
- 27. Con鍖ict detec-on John Anne hasApproved hasApproved narrower environment pollu=on broader hasApproved hasApproved Monique Delphine 27
- 28. Experimenta-on at ADEME <1=1&812) :+,) 516787691) !"#$%&'#() :;,) *+,) -../"012) 34,) 28
- 29. Folksonomy enrichment life-足cycle User-centric structuring Flat Automatic folksonomy processing Detect ADDING TAGS conflicts Global Structured structuring folksonomy 29
- 30. Global map Includes all points of view, highlights con鍖icts + consensuses 30
- 31. Referent choices Choices of the referent user (archivists at Ademe e.g.) 31
- 32. Folksonomy enrichment life-足cycle User-centric structuring Flat Automatic folksonomy processing Detect ADDING TAGS conflicts Structured Global folksonomy structuring 32
- 33. Each point of view corresponds to a layer 33
- 34. Enriching individual points of view Integra=ng others' contribu=ons: Anne is looking for 1. Current user -足> "Anne" resources tagged 2. ReferentUser (e.g. archivists) "environnement" 3. Con鍖ictSolver (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. 2. 2nd Applica-on case : Leveraging the reuse of 2nd hand objects 35
- 36. Paris How will I get rid of all these rusty co鍖ee makers?? Nice
- 37. Digitazing the stock of 2nd hand shops 37
- 38. Seman-cally Enhanced catalog The goal : Finding semantically ? Related tags ? To enhance searching ? coffee maker
- 39. Seman-cally Enhanced catalog The idea : Mapping tags With ontologies concepts Hot liquid container coffee pot coffee maker tea pot = subClassOf
- 40. Seman-cally Enhanced catalog 1. The user enter "co鍖ee maker" Results for "coffee maker": coffee maker 2. The system suggests addi=onal results thanks to seman=c rela=ons Related results : Results for "tea pot": Results for "coffee pot":
- 41. 5. Conclusion 41
- 42. What we do : Help online communi=es environment renewable energy related has broader wind-足energy structure their tags related has narrower sustainability wind turbine 42
- 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-足day tasks Implementa-on within ISICIL solu=on (tagging server) 43
- 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. Thank you for your aden-on ! me : freddy.limpens@inria.fr http://www-sop.inria.fr/members/Freddy.Limpens my advisors : Fabien Gandon : fabien.gandon@inria.fr Michel Bu鍖a : buffa@unice.fr ISICIL team : http://isicil.inria.fr 45