CSM06 Information Retrieval

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CSM06 Information Retrieval

• Lecture 6 Visualising the Results Set
• Dr Andrew Salway a.salway_at_surrey.ac.uk

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Recap of Lecture 5

• Algorithms that analysed link structure around a
  webpage to bring back related pages (Dean and
  Henzinger)
• A system to transform questions into queries
  TRITUS
• The evaluation of web search engines adapting
  precision/recall and other criteria

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Lecture 6 OVERVIEW

• Using information visualisation to help users of
  IR systems to better understand the results set
• Recent and current developments of web search
  technologies

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TileBars

• TileBars shows which query terms are where in
  which documents
• One row per query term
• One column per passage of text
• Shading indicates frequency of term in passage
• Users can look for terms co-occurring in passages
  and occurring throughout long documents

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InfoCrystal

• InfoCrystal displays the results of a faceted
  query simultaneously
• Limited to four query terms
• Shows number of documents in the intersections /
  unions of the query terms
• Users can look judge relative influence of facets
  on results set

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Some current RD issues for web search engines

• Constant efforts to improve user interfaces, both
  to help users express their information needs and
  to help them understand more about the results,
  i.e. clustering and information visualisation
• KartOO
• Vivisimo
• Google Labs gives some insights to potentially up
  and coming features

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KartOO metasearch engine with visual display

• According to http//www.kartoo.com/
• Analyses users query gets results from relevant
  engines selects best sites and places them on a
  map
• Sites are represented by more or less important
  size pages, depending on their relevance
• Move pointer over pages to illuminate associated
  keywords and display a description of the site
• A series of keywords is suggested to. refine
  search

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Vivisimo

• According to http//vivisimo.com/
• Vivísimo's search and clustering solutions are
  based on a powerful new approach that organizes
  search results into meaningful categories without
  requiring any preprocessing of documents. Our
  comprehensive content search and clustering
  solution requires no preexisting taxonomy, yet
  works to enhance existing taxonomies where they
  exist.

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Some recent developments in Google Labs
http//labs.google.com

• Desktop search
• search application that provides full text
  search over your email, computer files, chats,
  and the web pages you've viewed
• Since you can easily search information on your
  computer, you don't need to worry about
  organizing your files, email, or bookmarks

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Some recent developments in Google Labs
http//labs.google.com

• SMS search
• Google SMS (Short Message Service) enables you
  to easily get precise answers to specialized
  queries from your mobile phone or device. Send
  your query as a text message and get phone book
  listings, dictionary definitions, product prices
  and more. Just text. No links. No web pages.

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Some recent developments in Google Labs
http//labs.google.com

• Personalised Search
• Once youve entered a description of your general
  interests your search results are modified
  accordingly

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Some recent developments in Google Labs
http//labs.google.com

• Google Sets
• Enter a few items and a longer list (of the same
  kinds of things) is returned

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Set Reading for Lecture 6

• No set reading this week.

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Further Reading

• About TileBars
• Hearst (1995), TileBars Visualization of Term
  Distribution Information in Full Text Information
  Access, Proceedings of the ACM SIGCHI Conference
  on Human Factors in Computing Systems(CHI), pp.
  59-66, Denver, CO, May 1995.
• www.sims.berkeley.edu/hearst/papers/tilebars-chi9
  5/chi95.html
• About InfoCrystal
• http//www.scils.rutgers.edu/aspoerri/InfoCrystal
  /Ch_7.html

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Lecture 6 LEARNING OUTCOMES

• You should be able to
• Describe, compare and discuss the applicability
  of some ways in which visualisation of the
  results set can help users of IR systems
• Explain some of the current challenges facing the
  developers of web search engines, and comment on
  how recent RD is addressing these

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Reading ahead for LECTURE 7

• If you want to prepare for next weeks lecture
  then take a look at
• del Bimbo (1999), Visual Information Retrieval.
  Available in library article collection.
• Smith and Chang (1997), Visually Searching the
  Web for Content, IEEE Multimedia July-September
  1997, pp. 12-20. Available via librarys
  eJournal service.