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Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
Visual search for supporting content exploration in large document collections
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Visual search for supporting content exploration in large document collections

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  • 1.   Visual  search  for  suppor3ng  content  explora3on  in  large  document  collec3ons   Drahomira  Herrmannova  and  Petr  Knoth   1/45  
  • 2. Contents  •  What  do  we  do  •  Informa3on  Visualisa3ons  and  Visual  Search   Interfaces  •  Our  approach  •  Conclusion   2/45  
  • 3. Contents  •  What  do  we  do  •  Informa3on  Visualisa3ons  and  Visual  Search   Interfaces  •  Our  approach  •  Conclusion   3/45  
  • 4. What  do  we  do  •  Improve  search  in  (large)  document  collec3ons  •  Examples  of  collec3ons:   – News  ar3cles   – Cultural  heritage  collec3on   – Collec3on  of  scien3fic  papers  •  Current  search  engines:   – Support  for  lookup   – Much  less  support  for  explora3on   4/45  
  • 5. Search  tasks  (Rose  and  Levinson,  2004)  •  Undirected  (or  exploratory)  queries  –  significant   por3on  of  all  searches  (Rose  and  Levinson,  2004)   5/45  
  • 6. Exploratory  search  (Marchionini,  2006)   6/45  
  • 7. How  to  support  exploratory  search  •  One  possible  solu3on  –  informa3on   visualisa3on  •  Why?   –  Easier  to  communicate  structure,  organisa3on   and  rela3ons  in  content   –  Visually  appealing   7/45  
  • 8. Contents  •  What  do  we  do  •  Informa3on  Visualisa3ons  and  Visual  Search   Interfaces  •  Our  approach  •  Conclusion   8/45  
  • 9. Informa3on  Visualisa3on  (1/2)  •  Division  according  to  granularity  of   informa3on   –  Collec3on  level   –  Document  level   –  Intra-­‐document  level   9/45  
  • 10. Collec3on  level  visualisa3ons  •  Visualise  a^ributes  of  the  collec3on  •  Typically  aim  at  providing  a  general  overview   of  the  collec3on  content  •  Examples   10/45  
  • 11. Tag  clouds  (Montero  and  Solana,  2006)   11/45  
  • 12. TIARA  (Wei  et  al.,  2010)   12/45  
  • 13. GRIDL  (Schneiderman  et  al.,  2000)   13/45  
  • 14. Document  level  visualisa3ons  •  Visualise  a^ributes  of  the  collec3on  items  •  Mutual  links  and  rela3ons  of  collec3on  items  •  Examples   14/45  
  • 15. Hopara  (Milne  and  Wi^en,  2011)   15/45  
  • 16. Wivi  (Lehmann  et  al.,  2010)   16/45  
  • 17. Apolo  (Chau  et  al.,  2011)   17/45  
  • 18. Intra-­‐document  level  visualisa3ons  •  Visualise  the  internal  structure  of  a  document  •  Example   18/45  
  • 19. TileBars  (Hirst,  1995)   19/45  
  • 20. Informa3on  Visualisa3on  (2/2)  •  Division  according  to  the  “star3ng  point”  of   the  visualisa3on   –  Browsing  focused   –  Query  focused   20/45  
  • 21. Browsing  focused  •  Explora3on  starts  at  a  specific  point  in  the   collec3on  from  which  the  user  navigates   through  the  collec3on  •  Usually  the  same  star3ng  point  is  used  every   3me   21/45  
  • 22. InfoSky  (Granitzer  et  al.,  2004)   22/45  
  • 23. Query  focused  •  Starts  with  a  query  •  The  query  determines  the  entry  point  from   which  the  explora3on  starts   23/45  
  • 24. ThinkPedia  (Hirsch  et  al.,  2009)   24/45  
  • 25. Our  approach  •  Document  level  informa3on  •  Query  focused  browsing   25/45  
  • 26. Design  principles  (1/2)  •  For  visual  search  interfaces  •  Should  be  considered  when  designing  the   interface  •  Related  studies:   –  Chen  and  Yu,  2000   –  Sebrechts  et  al.,  1999   26/45  
  • 27. Design  principles  (2/2)  1.  Added  value  2.  Simplicity  3.  Visual  legibility  4.  Use  of  colours    5.  Dimension  6.  Fixed  spa3al  loca3on   27/45  
  • 28. Contents  •  What  do  we  do  •  Informa3on  Visualisa3ons  and  Visual  Search   Interfaces  •  Our  approach  •  Conclusion   28/45  
  • 29. Considered  types  of  collec3ons  •  Every  document  in  a  collec3on  defined   according  to  a  set  of  dimensions  •  Dimensions  typically  of  different  types  •  Document  =  set  of  proper3es  expressing   values  of  dimensions  •  Dimensions  always  present  •  Examples   29/45  
  • 30. News  ar3cles  collec3on  •  Dimensions:   –  Time   –  Themes   –  Loca3ons   –  Rela3ons  to  other  ar3cles   30/45  
  • 31. Cultural  heritage  ar3facts  •  Dimensions:   –  Ar3fact  type   –  Historical  period   –  Style   –  Material   31/45  
  • 32. Scien3fic  papers  •  Dimensions:   –  Cita3ons   –  Authors   –  Concepts   –  Similari3es  with  other  ar3cles   32/45  
  • 33. The  visualisa3on   33/45  
  • 34. Discovering  connec3ons   34/45  
  • 35. Comparing  and  contras3ng  documents   35/45  
  • 36. Limita3ons  •  In  theory  not  restricted,  the  limita3ons  might   be:   –  the  size  and  resolu3on  of  the  screen     –  the  limita3ons  of  human  percep3on   36/45  
  • 37. Contents  •  What  do  we  do  •  Informa3on  Visualisa3ons  and  Visual  Search   Interfaces  •  Our  approach  •  Conclusion   37/45  
  • 38. Conclusion  (1/2)  •  Mo3va3on:   1.  Provide  be^er  support  for  exploratory  search   than  current  textual  interfaces   2.  Interface  that  is  conceptually  applicable  in  any   document  collec3on  regardless  of  its  type   3.  Provide  an  added  value  by  assis3ng  in  the   discovery  of  interes3ng  connec3ons  that  would   otherwise  remain  hidden   38/45  
  • 39. Conclusion  (2/2)  •  Results:   1.  Support  for  comparing  and  contras3ng  content.   2.  Support  for  explora3on  across  dimensions.   3.  Universal  approach  to  the  visualised  dimensions.   39/45  
  • 40. Future  plans  •  Planned  release  end  of  June  •  Integra3on  with  CORE  system  •  Evalua3on   40/45  
  • 41. References  (1/4)  •  G.  Marchionini.  Exploratory  search:  from  finding  to  understanding.   Communica3ons  of  the  ACM  -­‐  Suppor3ng  exploratory  search.  2006.  •  D.  Rose  &  D.  Levinson.  Understanding  user  goals  in  web  search.   Proceedings  of  the  13th  conference  on  World  Wide  Web.  2004.  •  Yusef  Hassan-­‐Montero  and  Victor  Herrero-­‐Solana.  Improving  tag-­‐clouds  as   visual  informa?on  retrieval  interfaces.  In  MERIDA,  INSCIT2006   CONFERENCE.  2006.  •  Furu  Wei,  Shixia  Liu,  Yangqiu  Song,  Shimei  Pan,  Michelle  X.  Zhou,  Weihong   Qian,  Lei  Shi,  Li  Tan,  and  Qiang  Zhang.  Tiara:  a  visual  exploratory  text  an-­‐   aly?c  system.  In  Proceedings  of  the  16th  ACMSIGKDD  interna3onal   conference  on  Knowledge  discovery  and  data  mining.  2010.   41/45  
  • 42. References  (2/4)  •  Ben  Shneiderman,  David  Feldman,  Anne  Rose,  and  Xavier  Ferré  Grau.   Visualizing  digital  librarysearch  results  with  categorical  and  hierarchical   axes.  In  Proceedings  of  the  fiqh  ACM  conference  on  Digital  libraries.  2000.  •  Mar3  A.  Hearst.  TileBars:  Visualiza?on  of  Term  Distribu?on  Informa?on  in   Full  Text  Informa?on  Access.  In  the  Proceedings  of  the  ACM  SIGCHI   Conference  on  Human  Factors  in  Compu3ng  Systems.  1995.  •  David  Milne,  Ian  Wi^en.  A  link-­‐based  visual  search  engine  for  Wikipedia.   Proceeding  of  the  11th  annual  interna3onal  ACM/IEEE  joint  conference  on   Digital  libraries.  2011.  •  Simon  Lehmann,  Ulrich  Schwanecke,  and  Rolf  Dorner.  Interac?ve   visualiza?on  for  opportunis?c  explora?on  of  large  document  collec?ons.   Informa3on  Systems.  2010.   42/45  
  • 43. References  (3/4)  •  Duen  Horng  Chau,  Aniket  Ki^ur,  Jason  I.  Hong,  and  Christos  Faloutsos.   Apolo:  making  sense  of  large  network  data  by  combining  rich  user   interac?on  and  machine  learning.  In  Proceedings  of  the  2011  annual   conference  on  Human  factors  in  compu3ng  systems.  2011.  •  Michael  Granitzer,  Wolfgang  Kienreich,  Vedran  Sabol,  Keith  Andrews,  and   Werner  Klieber.  Evalua?ng  a  system  for  interac?ve  explora?on  of  large,   hierarchically  structured  document  repositories.  In  Proceedings  of  the  IEEE   Symposium  on  Informa3on  Visualiza3on.  2004.  •  Chris3an  Hirsch,  John  Hosking,  and  John  Grundy.  Interac?ve  visualiza?on   tools  for  exploring  the  seman?c  graph  of  large  knowledge  spaces.   Interfaces.  2009.     43/45  
  • 44. References  (4/4)  •  Chaomei  Chen  and  Yue  Yu.  Empirical  studies  of  informa?on  visualiza?on:  a   meta-­‐analysis.  Int.  J.  Hum.-­‐  Comput.  Stud.  2000.  •  Marc  M.  Sebrechts,  John  V.  Cugini,  Sharon  J.  Laskowski,  Joanna  Vasilakis,   and  Michael  S.  Miller.  Visualiza?on  of  search  results:  a  compara?ve   evalua?on  of  text,  2d,  and  3d  interfaces.  In  Proceedings  of  the  22nd   annual  interna3onal  ACM  SIGIR  conference  on  Research  and  development   in  informa3on  retrieval.  1999.   44/45  
  • 45. Thanks  for  listening!     Ques3ons?   45/45  

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