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Considering Web Accessibility in Information Retrieval Systems

  • Myriam Arrue
  • Markel Vigo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4607)

Abstract

Search engines are the most common gateway for information searching in the WWW. Since Information Retrieval systems do not take web accessibility into account, results displayed are not useful for users with disabilities. We present a framework that includes the requirements to overcome this situation. It is composed of three modules: Content Analysis Module, Accessibility Analysis Module and Results Collector Module. This framework facilitates the implementation of search engines which return results ranked according to accessibility level as well as content relevance. Since criteria to sort results by their accessibility are necessary, we define accurate quantitative accessibility metrics which can be automatically calculated exploiting results yielded by any automatic evaluation tool. A prototype based on these requirements has been implemented to show the validity of the proposal.

Keywords

Information Retrieval System Accessibility Level Accessibility Evaluation Test File Accessibility Guideline 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Abascal, J., Arrue, M., Fajardo, I., Garay, N., Tomás, J.: Use of Guidelines to automatically verify web accessibility. International Journal of Universal Access in the Information Society 3(1), 71–79 (2004)CrossRefGoogle Scholar
  2. 2.
    Andronico, P., Buzzi, M., Castillo, C., Leporini, B.: Improving search engine interfaces for blind users: a case study. International Journal of Universal Access in the Information Society 5(1), 23–40 (2006)CrossRefGoogle Scholar
  3. 3.
    Arrue, M., Vigo, M., Abascal, J.: Quantitative Metrics for Web Accessibility Evaluation. In: Lowe, D.G., Gaedke, M. (eds.) ICWE 2005. LNCS, vol. 3579, Springer, Heidelberg (2005)Google Scholar
  4. 4.
    Brajnik, G.: Comparing accessibility evaluation tools: a method for tool effectiveness. International Journal of Universal Access in the Information Society 3(3-4), 252–263 (2004)CrossRefGoogle Scholar
  5. 5.
    Bühler, C., Heck, H., Perlick, O., Nietzio, A., Ullveit-Moe, N.: Interpreting Results from Large Scale Automatic Evaluation of Web Accessibility. In: Miesenberger, K., Klaus, J., Zagler, W., Karshmer, A.I. (eds.) ICCHP 2006. LNCS, vol. 4061, pp. 184–191. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Caldwell, B., Chisholm, W., Slatin, J., Vanderheiden, G. (eds.). (2006, April 27) Web Content Accessibility Guidelines 2.0. (Working Draft), http://www.w3.org/TR/WCAG20/
  7. 7.
    Chisholm, W., Vanderheiden, G., Jacobs, I. (eds.): (May 5, 1999), Web Content Accessibility Guidelines 1.0. http://www.w3.org/TR/WAI-WEBCONTENT/
  8. 8.
    Hackett, S., Parmanto, B., Zeng, X.: Accessibility of Internet websites through time. In: Proceedings of 6th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 32–39. ACM Press, New York (2004)CrossRefGoogle Scholar
  9. 9.
    International Organization of Standardization (ISO), Information Technology - Software Product Evaluation (ISO 14598). Geneva, Switzerland (1999)Google Scholar
  10. 10.
    International Organization of Standardization (ISO), Software Engineering - Product Quality - Part1: Quality Model (ISO 9126-1). Geneva, Switzerland (2001)Google Scholar
  11. 11.
    Ivory, M.Y., Hearst, M.A.: The state of art in automating usability evaluations of user interfaces. ACM Computing Surveys 33(4), 470–516 (2001)CrossRefGoogle Scholar
  12. 12.
    Ivory, M.Y., Yu, S., Gronemyer, K.: Search result exploration: a preliminary study of blind and sighted users’ decision making and performance. In: CHI Extended Abstracts, pp. 1453–1456 (2004) Google Scholar
  13. 13.
    Kobayashi, M., Takeda, K.: Information Retrieval on the Web. ACM Computing Surveys 32(2), 144–173 (2000)CrossRefGoogle Scholar
  14. 14.
    Leporini, B., Paternò, F., Scorcia, A.: Flexible tool support for accessibility evaluation. Interacting with Computers 18(5), 869–890 (2006)CrossRefGoogle Scholar
  15. 15.
    Mich, L., Franch, M., Gaio, L.: Evaluating and Designing Web Site Quality. IEEE Multimedia 10(1), 34–43 (2003)CrossRefGoogle Scholar
  16. 16.
    Olsina, L., Rossi, G.: Measuring Web Application Quality with WebQEM. IEEE Multimedia 9(4), 20–29 (2002)CrossRefGoogle Scholar
  17. 17.
    Snaprud, M.H, Ulltveit-Moe, N., Pillai, A.B., Olsen, M.G.: A Proposed Architecture for Large Scale Web Accessibility Assessment. In: Miesenberger, K., Klaus, J., Zagler, W., Karshmer, A.I. (eds.) ICCHP 2006. LNCS, vol. 4061, pp. 234–241. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Sullivan, T., Matson, R.: Barriers to use: usability and content accessibility on the Web’s most popular sites. In: Proceedings of the ACM Conference on Universal Usability 2000, pp. 139–144. ACM Press, New York (2000)CrossRefGoogle Scholar
  19. 19.
    Vanderdonckt, J., Bereikdar, A.: Automated Web Evaluation by Guideline Review. Journal of Web Engineering 4(2), 102–117 (2005)Google Scholar
  20. 20.
    Zhu, X., Gauch, S.: Incorporating quality metrics in centralized/distributed information retrieval on the World Wide Web. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 288–295. ACM Press, New York (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Myriam Arrue
    • 1
  • Markel Vigo
    • 1
  1. 1.University of the Basque Country, Informatika Fakultatea, Manuel Lardizabal 1, E-20018, DonostiaSpain

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