Advertisement

Free-Text Search over Complex Web Forms

  • Kien Tjin-Kam-Jet
  • Dolf Trieschnigg
  • Djoerd Hiemstra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6653)

Abstract

This paper investigates the problem of using free-text queries as an alternative means for searching ‘behind’ web forms. We introduce a novel specification language for specifying free-text interfaces, and report the results of a user study where we evaluated our prototype in a travel planner scenario. Our results show that users prefer this free-text interface over the original web form and that they are about 9% faster on average at completing their search tasks.

Keywords

query processing free-text interfaces query translation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sun, J., Bai, X., Li, Z., Che, H., Liu, H.: Towards a wrapper-driven ontology-based framework for knowledge extraction. In: Zhang, Z., Siekmann, J.H. (eds.) KSEM 2007. LNCS (LNAI), vol. 4798, pp. 230–242. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Kaufmann, E., Bernstein, A.: Evaluating the usability of natural language query languages and interfaces to semantic web knowledge bases. In: Web Semantics: Science, Services and Agents on the World Wide Web (2010)Google Scholar
  3. 3.
    Papakonstantinou, Y., Gupta, A., Garcia-Molina, H., Ullman, J.D.: A query translation scheme for rapid implementation of wrappers. In: Ling, T.-W., Vieille, L., Mendelzon, A.O. (eds.) DOOD 1995. LNCS, vol. 1013, pp. 161–186. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  4. 4.
    Appelt, D.E., Onyshkevych, B.: The common pattern specification language. In: Proceedings of a Workshop on Held at Baltimore, Maryland, Morristown, NJ, USA, pp. 23–30. Association for Computational Linguistics (1996)Google Scholar
  5. 5.
    Madhavan, J., Ko, D., Kot, L., Ganapathy, V., Rasmussen, A., Halevy, A.: Google’s deep web crawl. In: Proc. VLDB Endow., vol. 1(2), pp. 1241–1252 (2008)Google Scholar
  6. 6.
    White, R.W., Marchionini, G.: Examining the effectiveness of real-time query expansion. Information Processing and Management 43(3), 685–704 (2007)CrossRefGoogle Scholar
  7. 7.
    Kutner, M.H., Nachtsheim, C.J., Neter, J., Li, W.: Applied linear statistical models, 5th edn. McGraw-Hill, New York (2005)Google Scholar
  8. 8.
    Kendall, M.: Rank Correlation Methods, 4th edn. Second impression. Charles Griffin (1975)Google Scholar
  9. 9.
    Alba, A., Bhagwan, V., Grandison, T.: Accessing the deep web: when good ideas go bad. In: OOPSLA Companion 2008: Companion to the 23rd ACM SIGPLAN Conference on Object-Oriented Programming Systems Languages and Applications, pp. 815–818. ACM, New York (2008)Google Scholar
  10. 10.
    Meng, F.: A natural language interface for information retrieval from forms on the world wide web. In: ICIS, Atlanta, GA, USA, pp. 540–545. Association for Information Systems (1999)Google Scholar
  11. 11.
    Weizenbaum, J.: Eliza—a computer program for the study of natural language communication between man and machine. Commun. ACM 9(1), 36–45 (1966)CrossRefGoogle Scholar
  12. 12.
    Dar, S., Entin, G., Geva, S., Palmon, E.: Dtl’s dataspot: Database exploration using plain language. In: Proceedings of the 24th International Conference on Very Large Data Bases, VLDB 1998, pp. 645–649. Morgan Kaufmann Publishers Inc, San Francisco (1998)Google Scholar
  13. 13.
    Demidova, E., Fankhauser, P., Zhou, X., Nejdl, W.: Divq: diversification for keyword search over structured databases. In: SIGIR 2010, pp. 331–338. ACM, New York (2010)Google Scholar
  14. 14.
    Tran, T., Cimiano, P., Rudolph, S., Studer, R.: Ontology-based interpretation of keywords for semantic search. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 523–536. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Zhou, Q., Wang, C., Xiong, M., Wang, H., Yu, Y.: Spark: adapting keyword query to semantic search. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 694–707. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Tata, S., Lohman, G.M.: Sqak: doing more with keywords. In: SIGMOD 2008, pp. 889–902. ACM, New York (2008)Google Scholar
  17. 17.
    Kandogan, E., Krishnamurthy, R., Raghavan, S., Vaithyanathan, S., Zhu, H.: Avatar semantic search: a database approach to information retrieval. In: SIGMOD 2006, pp. 790–792. ACM, New York (2006)Google Scholar
  18. 18.
    Burton, R.R.: Semantic grammar: An engineering technique for constructing natural language understanding systems. Technical report, Bolt, Beranek and Newman, Inc., Cambridge, MA (December 1976)Google Scholar
  19. 19.
    Hendrix, G.G., Sacerdoti, E.D., Sagalowicz, D., Slocum, J.: Developing a natural language interface to complex data. ACM TODS 3(2), 105–147 (1978)CrossRefGoogle Scholar
  20. 20.
    Carbonell, J.G., Boggs, W.M., Mauldin, M.L., Anick, P.G.: The XCALIBUR project: a natural language interface to expert systems. In: IJCAI 1983, pp. 653–656. Morgan Kaufmann Publishers Inc., San Francisco (1983)Google Scholar
  21. 21.
    Carbonell, J.G., Hayes, P.J.: Dynamic strategy selection in flexible parsing. In: Proceedings of the 19th Annual Meeting on ACL, Morristown, NJ, USA, pp. 143–147. Association for Computational Linguistics (1981)Google Scholar
  22. 22.
    Androutsopoulos, I., Ritchie, G.D., Thanisch, P.: Natural language interfaces to databases – an introduction. Natural Language Engineering 1(01), 29–81 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kien Tjin-Kam-Jet
    • 1
  • Dolf Trieschnigg
    • 1
  • Djoerd Hiemstra
    • 1
  1. 1.University of TwenteEnschedeThe Netherlands

Personalised recommendations