1 / 24

CSM06 Information Retrieval

CSM06 Information Retrieval. Lecture 6: Visualising the Results Set Dr Andrew Salway a.salway@surrey.ac.uk. 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

mei
Download Presentation

CSM06 Information Retrieval

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. CSM06 Information Retrieval Lecture 6: Visualising the Results Set Dr Andrew Salway a.salway@surrey.ac.uk

  2. 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

  3. 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

  4. 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

  5. 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

  6. Some current R&D 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…

  7. KartOO – metasearch engine with visual display According to http://www.kartoo.com/ • Analyses user’s 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

  8. 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.”

  9. 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”

  10. 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.”

  11. Some recent developments in Google Labs: http://labs.google.com Personalised Search Once you’ve entered a description of your general interests your search results are modified accordingly

  12. 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…

  13. Set Reading for Lecture 6 No set reading this week.

  14. 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-chi95/chi95.html • About InfoCrystal: http://www.scils.rutgers.edu/~aspoerri/InfoCrystal/Ch_7.html

  15. 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 R&D is addressing these

  16. Reading ahead for LECTURE 7 If you want to prepare for next week’s 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 library’s eJournal service.**

More Related