1 / 33

Individualized Knowledge Access

Individualized Knowledge Access. David Karger Lynn Andrea Stein Mark Ackerman Ralph Swick. Information Access. A key task in Oxygen: help people manage and retrieve information Three overlapping projects: Haystack: information storage and retrieval application clients

aloha
Télécharger la présentation

Individualized Knowledge Access

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Individualized Knowledge Access David Karger Lynn Andrea Stein Mark Ackerman Ralph Swick

  2. Information Access • A key task in Oxygen: help people manage and retrieve information • Three overlapping projects: • Haystack: • information storage and retrieval • application clients • Semantic Web: next-generation metadata • Volt: collaborative access

  3. Presentation Overview • Motivation • Information access behavior and goals • System Design & Architecture • Data Model • Interacting data and UI components • Working applications • Base haystack • Frontpage • Volt

  4. Motivation

  5. Problem Scenario • I try solving problems using my data: • Information gathered personally • High quality, easy for me to understand • Not limited to publicly available content • My organization: • Personal annotations and meta-data • Choose own subject arrangement • Optimize for my kind of searching • Adapts to my needs

  6. Then Turn to a Friend • Leverage • They organize information for their own use • Let them find things for me too • Shared vocabulary • They know me and what I want • Personal expertise • They know things not in any library • Trust • Their recommendations are good

  7. Last to Library/web • Answer usually there • But hard to find • Wish: rearrange to suit my needs • Wish: help from my friends in looking

  8. Lessons • Individualized access • Best tools adapt to individual ways of organizing and seeking data • Individualized knowledge • People know more than they publish • That knowledge is useful to them and others • Collaborative use • Right incentives lead to sharing and joint use

  9. Haystack • Individualized access • My data collection, organization • Search tools tuned for me • Collaborate to leverage individual knowledge • Access unpublished information in others’ haystacks • Self interest public benefit • Lens to personalize access to the world library • Rearrange presentation to suit my personal needs

  10. Example • Info on probabilistic models in data mining • My haystack doesn’t know, but “probability” is in lots of email 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 • Some are by Daphne Koller • I read/liked other work by Koller • My Haystack queries “Daphne Koller Bayes” on Yahoo • Tommi’s haystack can rank the results for me…

  11. System Design

  12. Gathering Data • Haystack archives anything • Web pages browsed, email sent and received, address book, documents written • And any properties, relationships • Text of object (for text search) • Author, title, color, citations, quotations, annotations, quality, last usage • Users freely add types, relationships

  13. Doc Haystack D. Karger Outstanding Semantic Web • Arbitrary objects, connected by named links • No fixed schema • User extensible • Sharable by any application • A new “file system”? HTML type title quality author says

  14. Gathering Data • Active user input • Interfaces let user add data, note relationships • Mining data from prior data • Plug-in services opportunistically extract data • Passive observation of user • Plug-ins to other interfaces record user actions • Other Users

  15. Spider Machine Learning Services Web Viewer Volt Viewer/ Editor Web Observer Proxy Mail Observer Proxy Data Extraction Services Triple Store Deduction Clients Data Sources

  16. Sample Applications

  17. Sample Applications • Because everything uses the Semantic Web constructions, a variety of application clients can share information • Web Browser---data viewer • FrontPage---personalized information filter • Volt---collaboration tool

  18. Haystack via Web • Web server interface • Basic operations: • Insert objects • View objects • Queries

  19. Haystack via Web

  20. Haystack via Web • Viewer shows one node and associated arrows • Service notices we’ve archived a directory; so archives the objects it contains (and so on…)

  21. Haystack via Web • Services detect document type, extract relevant metadata • Output can specialize by type of object

  22. Mediation • Haystack can be a lens for viewing data from the rest of the world • Stored content shows what user knows/likes • Selectively spider “good” sites • Filter results coming back • Compare to objects user has liked in the past • Can learn over time • Example - personalized news service

  23. News Service

  24. News Service • Scavenges articles from your favorite news sources • Html parsing/extracting services • Over time, learns types of articles that interest you • Prioritizes those for display • Content provider no longer controls viewing experience • No more ads

  25. Personalized News Service

  26. Collaborative Access • Want to leverage others’ work in organizing information • No need to “publish” expertise • Exposed automatically---without effort • Self interest helps others

  27. Volt • Volt is about collaboration between people • The Haystack architecture allows easy collaboration among individuals • semantic web references to Haystack objects • Individuals share parts of their Haystack • Group spaces and shared notebooks

  28. Volt

  29. Collaborators • Those I interact with • Frequent mail contact • Frequent visits to their home page • Those with shared content • And who have same opinions about content • Collaborative filtering techniques • Referrals • Expertise search engine

  30. Expertise Beacon

  31. Volt Expertise Beacons • Group spaces and shared notebooks • Create individual and group profiles • Profiles can be used to find other people • Allows targeted search • “Who else is working on this project?” • User controls visibility/privacy

  32. Summary • Next generation information access • Semantic Web • provides a language and capabilities for meta-data • Haystack • teases out individual knowledge, • stores it in a coherent fashion, and • allows a variety of application clients to leverage individual meta-data • Volt • turns individual knowledge into a community resource

  33. More Info http://haystack.lcs.mit.edu/ http://www.w3c.org/2001/sw karger@mit.edu las@ai.mit.edu ackerman@lcs.mit.edu swick@w3.org

More Related