Extracting and capturing knowledge found in social software tools

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    Extracting and capturing knowledge found in social software tools - Presentation Transcript

    1. Extracting and capturing knowledge found in social software tools Neil Richards KM Consultant www.knowledgethoughts.com
    2. Today?
      • How law firms are using web2.0
      Quality control Indexing, archiving and distributing Extracting structured data
    3. How law firms are using web2.0?
    4. No single tool
      • Social networking
      • Wikis
      • Blogs
      • RSS
      • Tagging
      Instead…
    5. Different needs Client knowledge Idea sharing Communications Managing Projects Team knowledge Subject knowledge Firm knowledge
    6. Examples: Magic circle  team wikis, practice wikis and “Pedias" Dewey & Leboeuf  wikis, blogs, rss & social bookmarking The Rosen Law Firm  wikis for reports, profile pages, communications Goodwin Proctor  wikis for managing projects, clients & legal knowledge Dykema  RSS, communication, information distribution Over 25% of the AMLaw 200  Client facing blogs
    7. Extracting structured information
      • garbage in  garbage out
      • To extract useful knowledge you first have to have some
    8. It requires the contributions of many
    9. But lawyers are often too busy…
    10. … or doing other things
    11. Nobody likes filling out forms…
      • … and structured mark-up is too complex.
      '''Berlin''' is the capital of [[capital of::Germany]] and also its largest city; the city is now home to [[population::3,391,407]], down from a peak of 4.5 million before [[World War II]]. It measures [[area::891.69 square kilometers]] and has the coordinates [[coordinates::52°31'N; 13°24'E]]. Berlin is located in the north of [[located in::Germany]] [[Category:City]][[Category:sample pages]] {{#ask: [[Category:City]] [[located in::Germany]] | ?population | ?area#km² = Size in km² }}
    12. Submitting knowledge needs to be simple
    13. Order from chaos Tags / categories Favourites Social ratings Usage stats Enrichment Data analysis Social platforms
    14. Quality control
    15. Monitor key content Track changes Watch usage
      • Have some content PSL & Partner approved
      • Embed visual indicators of quality
      • Make the names of contributors visible
      • Content-appropriate levels of review
    16. Indexing and archiving Low volume – high value. You can’t index everything. Take usage into account Where possible, track “expiry dates” and have a process to retire knowledge
      • Distribution
    17. Additional information & reference material http://www.knowledgethoughts.com/blog/?page_id=215 Photo credits http://www.sxc.hu/photo/787433 http://www.sxc.hu/photo/938865 http://www.sxc.hu/photo/604247 http://www.sxc.hu/photo/989421 http://www.sxc.hu/photo/334026 http://www.sxc.hu/photo/1088897 http://www.sxc.hu/photo/304293 http://www.sxc.hu/photo/292426 http://www.sxc.hu/photo/253955 http://www.sxc.hu/photo/1005156 http://www.sxc.hu/photo/839415 http://www.sxc.hu/photo/765550 http://www.sxc.hu/photo/590355 http://www.sxc.hu/photo/468027 http://www.sxc.hu/photo/761191 http://www.sxc.hu/photo/821903 http://www.sxc.hu/photo/1032278 http://www.sxc.hu/photo/182201 http://www.sxc.hu/photo/819168 http://www.sxc.hu/photo/866529 http://www.sxc.hu/photo/647502 http://www.sxc.hu/photo/428182 http://www.sxc.hu/photo/575060 http://www.sxc.hu/photo/970189 http://www.sxc.hu/photo/1080889 http://www.sxc.hu/photo/949308 http://www.sxc.hu/photo/228778 Neil Richards KM Consultant www.knowledgethoughts.com

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