Research on collaborative information sharing systems

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  • 1. Research on collaborative information sharing systems
      • Davide Eynard
      • [email_address]
      • Dipartimento di Elettronica e Informazione
      • Politecnico di Milano
  • 2. Intro
    • What I'm doing
    • Collaborative systems
    • Semantic Web
    • Semantic Wikis
    • Folksonomies
  • 3. What I'm doing
    • “Research on collaborative information sharing systems”
      • They are designed to help people involved in a common task achieve their goals
      • Actually, they do not only allow for information sharing, but also for collaborative work
      • There are plenty of them: we are focusing on some we call participative
  • 4. Collaborative encyclopedias
  • 5. Collaborative playlists
  • 6. Collaborative music
  • 7. Collaborative docs
  • 8. Collaborative maps
  • 9. Collaborative slide collections
  • 10. Collaborative word processing
  • 11. Collaborative bookmarking
  • 12. Collaborative news
  • 13. Why?
    • Why are collaborative systems having so much success lately?
      • Give a look at “ What is Web2.0 ” design patterns:
        • The long tail
        • Data is the next Intel Inside
        • Users add value
        • Network effects by default
        • Some rights reserved
        • The perpetual beta
        • Cooperate, don't control
        • Software above the level of the single device
  • 14. How?
    • How can you make your users actively contribute to a project?
      • Instant gratification
        • i.e. Napster, CDDB (now FreeDB, MusicBrainz)
      • Communities of practice ( Etienne Wenger )
      • User interface
    • So, systems should
      • be very easy to use
      • provide an immediate reward
      • do most of the hard work automatically
  • 15. What?
    • What do participative systems and Semantic Web have in common?
  • 16. What?
    • What do participative systems and Semantic Web have in common?
    “ The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation” Tim Berners-Lee, 2001
  • 17. What is Semantic Web?
    • HAH! 10^9$ Question!
    • Semantic Web is not “making machines understand stuff”
    • Semantic Web is about
      • standards
      • reasoning
      • interoperability
      • metadata (client-server-server model)
        • ie. annotations, classifications, ratings, etc.
  • 18. Glasses
  • 19. PowerGlasses
  • 20. And now...
    • A little presentation by this (great) man:
    • Michael Wesch
      • Assistant professor of Cultural Anthropology
      • Kansas State University
  • 21. So again... what?
    • What do participative systems and Semantic Web have in common?
      • Participative systems work thanks to contributions by people
        • unstructured information, by humans for humans
        • hard to find and organize
      • Semantic Web works thanks to structured information
        • not easy to publish, need more participation
      • So, why don't we use
        • semantics to help people organize and find stuff
        • people to help Semantic Web bootstrap
  • 22. Our work
    • Our work is focused on two main fields:
      • Semantic Wikis
        • Wiki as in “Wikipedia”...
        • ... but also (well, mostly) in enterprises
      • Folksonomies
        • expanding them with ontologies
        • relations between users, resources, tags
        • description of different families of tags
        • applications
  • 23. Semantic Wikis
    • Wikis are one of the best examples of “read/write” Web
      • they allow any user to easily create, modify, delete any page
      • but the information is just plain, unstructured text: no interoperability, no way to organize them
  • 24. Current approaches on SWikis
    • semantics added inside pages
      • pages as concepts and relations within them
    • semantics added as metadata
      • tags which describe the pages
    • our approach: semantics on different levels
      • wiki system
      • context
      • contents
      • upper ontology
  • 25. Folksonomies
    • Term by Thomas Vander Wal (2004)
      • “folks” + “taxonomy”
    • First (and most cited) websites:
      • flickr
      • del.icio.us
    • Growing interest
      • users increase exponentially (1.5M in deli)
  • 26. Advantages of folksonomies
    • Folksonomies:
      • are inclusive
      • are current
      • offer discovery
      • are non-binary
      • are democratic and self-moderating
      • follow “desire lines”
      • offer insight into user behavior
      • are usable with a low cost
  • 27. Limits of folksonomies
    • Limits of folksonomies:
      • no synonym control
      • “basic level” variations
      • lack of precision
      • lack of recall (!)
      • lack of hierarchy
      • gaming
      • no real standard
  • 28. Limits of folksonomies
  • 29. Limits of folksonomies
  • 30. Limits of folksonomies
  • 31. Studying folksonomies
    • Tag usage and tag families
    • Expanding folksonomies with ontologies
    • Fuzzy queries inside folksonomies
  • 32. Tag usage
    • Power law distribution
  • 33. Tag usage
    • “Words with meaning”
    - 114 recognized words out of the 140 most used tags (81.43%) - follow power law distribution
  • 34. Tag families
    • Identifying what (or who) it is about
    • Identifying what it is
    • Identifying who owns it
    • Refining categories
    • Identifying qualities or characteristics
    • Self reference
    • Task organizing
  • 35. Fuzzy queries
  • 36. Fuzzy queries
  • 37. Fuzzy queries
    • The basic idea is that we could model resources belonging to one or more categories through fuzzy sets instead of crisp ones
    • To assign the membership value of a particular resource r with respect to a particular tag t , we calculate the ratio
    #users who tagged r as “ t” #users who saved r using any tag
  • 38. Fuzzy queries
    • Then we describe our fuzzy set with five fuzzy labels
  • 39. Fuzzy queries: results
    • A way to describe resources through tags
  • 40. Fuzzy queries: results
    • A way to describe resources through tags
    • A way to query folksonomies with a more intuitive interface to filter information
      • ie. search for “very programming and not much java”
  • 41. Fuzzy queries: results
    • A way to describe resources through tags
    • A way to query folksonomies with a more intuitive interface to filter information
      • ie. search for “very programming and not much java”
    • A way to learn something more about tag families
      • ie. tag “toread”
  • 42. Fuzzy queries: results
    • A way to describe resources through tags
    • A way to query folksonomies with a more intuitive interface to filter information
      • ie. search for “very programming and not much java”
    • A way to learn something more about tag families
      • ie. tag “toread”
    • A major drawback:
      • the system is quite slow
  • 43. That's All, Folks Thank you! Questions are welcome