Research on collaborative information sharing systems

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    Research on collaborative information sharing systems - Presentation Transcript

    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

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