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Lecture 10

Lecture 10. Visual Tools for Text Retrieval (cont.). How to Use Visualization to Support Retrieval. Source Thomas Mann (PhD Thesis Uni of Konstanz) Visualization of search results from the World Wide Web

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Lecture 10

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  1. Lecture 10 • Visual Tools for Text Retrieval (cont.)

  2. How to Use Visualization to Support Retrieval • Source • Thomas Mann (PhD Thesis Uni of Konstanz) • Visualization of search results from the World Wide Web • http://www.ub.uni-konstanz.de/v13/volltexte/2002/751//pdf/Dissertation_Thomas.M.Mann_2002.V.1.07.pdf

  3. Visualization of Document Attributes

  4. Document Visualization • Text not pre-attentive • Text = Abstract Concepts = Very High Dimensionality • Multiple & ambiguous meanings • Combinations of abstract concepts more difficult to visualize • Different combinations imply different meanings • Language only hints at meaning  based on common understanding “How much is that doggy in the window?” • Facilitate Information Retrieval • Collection Overview • Visualize which parts of query satisfied by document / collection • Understand why documents retrieved • Cluster Documents Based on Words in Common • Finds overall similarities among groups of documents • Picks out some themes, ignores others • Map Clusters onto 2D or 3D Representation • Minimize time/effort to decide which documents to examine

  5. Thumbnails SeeSoft Visualization of Document Attributes

  6. Visualization of Document Attributes Document Lens • Problem: Text can be too small to read  Focus + Context Fisheye Distortion Does it work?

  7. Visualization of Document Attributes  Perspective Wall

  8. Visualization of Document Attributes  Bar Graphs [Veerasamy 1996] / [Veerasamy, Belkin 1996]

  9. Visualization of Document Attributes  Stacked Bar Graphs VQRa of the WInquery system[Shneiderman, Byrd, Croft 1997]

  10. Visualization of Document Attributes  NIRVE & R-Wheels

  11. Visualization of Document Attributes  NIRVE

  12. Visualization of Document Attributes  NIRVE

  13. Visualization of Document Attributes  RankSpiral

  14. Visualization of Document Attributes  RankSpiral

  15. Visualization of Document Attributes  TileBars

  16. Visualization of Document Attributes  Table Lens

  17. Visualization of Document Attributes  Parallel Coordinates

  18. How to Use Visualization to Support Retrieval

  19. Visualization of Inter-Document Similarities

  20. Visualization of Inter-Document Similarities  DataMountain Download Video(30MB+ … will take a while) or http://www.scils.rutgers.edu/~aspoerri/Teaching/InfoVisResources/videos/ and right click on “datamtn.mpeg” and save

  21. Visualization of Inter-Document Similarities  VIBE

  22. Visualization of Inter-Document Similarities  Visual Who

  23. Visualization of Inter-Document Similarities  Cluster Bull’s Eye Google • Radial Mapping • Radius = Total Ranking • Angle Reflects Rankings • Size = Rankings by Engines Teoma MSN Lycos AltaVista

  24. Visualization of Inter-Document Similarities  Grokker Grokker

  25. Visualization of Inter-Document Similarities  Galaxy of News

  26. Visualization of Inter-Document Similarities  Self-Organizing Map

  27. Visualization of Inter-Document Similarities  Map.net Using VisualNet developed bywww.antarctica.net

  28. Visualization of Inter-Document Similarities  Kartoo Kartoo - Kvisu

  29. Visualization of Inter-Document Similarities  ThemeScapes

  30. Example: Themescapes(Wise et al. 95)

  31. Visualization of Inter-Document Similarities  ThemeScape

  32. Visualization of Inter-Document Similarities  ThemeScape Acquired byMicroPatentand calledAureka

  33. Visualization of Inter-Document Similarities  Lighthouse

  34. Visualization of Inter-Document Similarities  Lighthouse Cluster Hypothesis – related documents relevant to same requestsand should be in close spatial proximity.

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