Individualized%20Knowledge%20Access

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Individualized Knowledge Access

• David Karger
• Lynn Andrea Stein

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Web Search Tools

• Indices
• search by keyword
• Taxonomies
• classify by subject
• Cool site of the day

• A lot like libraries...
• Library catalogues
• Dewey Digital
• New book shelf, suggested reading

Is a universal library enough?
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Library/Web Limitations

• Huge
• too many answers, mostly irrelevant
• Only published material
• miss info known to few, leading-edge content
• Rigid
• all get same search results
• even if come back and try again

The library is the last place we look
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Bookshelves First

• My data
• information gathered personally
• high quality, easy for me to understand
• not limited to publicly available content
• annotations
• My organization
• choose own subject arrangement
• optimize for my kind of searching
• Adapts to my needs

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Then a Friend

• Leverage
• they organize information for their access
• so quickly find things for me
• Personal expertise
• they know things not in any library
• Trust
• their recommendations are good
• Shared vocabulary
• they know me and what I want

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Last the Library

• Answer usually there
• but hard to find
• And serendipity is nice too
• find stuff you werent seeking
• But even in library, individual matters
• specialized reading rooms
• personal cubicles
• ask librarian to use their expertise

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Lessons

• Individualized access The best tools adapt to
  individual ways of organizing and seeking data.
• Individualized knowledge People know, and are
  willing to share, much more than they publish.
  That knowledge can help others.

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Haystack a Tool for Oxygen

• Independent but interacting repositories that
  adapt to their individual users
• Individualize access
• My data collection, organization
• My search tools, with answers for me
• Leverage individual knowledge
• Collaborative retrieval with others
• Motivate people to organize their data for their
  own benefit and thus for others

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Example

• Have probabilistic models been used in data
  mining?
• My haystack doesnt know, but probability is in
  lots of mail I got from Tommi Jaakola
• Tommi told his haystack that Bayesian refers to
  probability models
• Tommi has read several papers on Bayesian methods
  in data mining
• His haystack suggests them to mine

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Research Threads

• Heterogeneous data and metadata
• archive whatever user wants
• Human-Computer Interaction
• let user express/use own organizational rules
• observe user to detect unexpressed knowledge
• Machine learning
• use gathered data to improve performance
• Collaborative filtering
• use others decisions to help me

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My data

• Haystack archives anything
• web pages browsed, email sent and received,
  documents written, scanned images, home
  directory, people known, projects worked on
• And any properties, relationships
• text of object (if know how)
• author, title, color, citations, quotations,
  annotations, quality, last usage
• User freely adds types, relationships

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Gathering My Data

• Active user input
• interfaces let user add data, note relationships
• Mining data from haystack
• plug-in services opportunistically extract data
• e.g., find author/title/text in MSWord document
• or, detect that one document quotes another
• Observing user
• plug-ins to other interfaces report user actions
• web pages browsed, mail sent, queries made

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Adaptation

• Remember users attempts to tune a query
• instead of first query attempt, use last one
• record items user picked as good matches
• future similar queries do better right away
• Stored content shows what user knows/likes
• modify queries to big search engines
• filter results coming back
• personalized cool site of the day

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Collaborative Access

• Leverage others work organizing data
• no need to publish expertise
• self interest helps others
• Privacy/permission concerns
• allowing exposure easier than publishing
• much prior work on permission schemes
• much public info mailing lists, papers read
• Whose opinions matter?
• people I mail, w/shared data, referrals
• collaborative filtering techniques

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Conclusion

• Libraries are not enough
• Haystack teases out individual knowledge
• Individualizes information access for user
• Shares individual knowledge to benefit community
• Current status individual-user prototype. Some
  data extraction, observation, adapting.
  Collaborative version in future.