Advertisement

Efficient Food Retrieval Techniques Considering Relative Frequencies of Food Related Words

  • Gwangbum Pyun
  • Unil Yun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6935)

Abstract

The scale of the Internet has become vast in the aspect of information. information. And, the performance of internet information retrieval systems are advanced. Now, researches of IIR (Internet Information Retrieval)systems are analysis of means of webpage based on keyword search. Recently, IIR system’s issue is system of searching necessary information for user. In this paper, we propose which search webpage of food related information and servicing the IIR system. Our system shows good performance than Commercial IIR services. We expect our system will be use-full IIR system.

Keywords

food information search engine ranking 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Akiko, A.: An information-theoretic perspective of tf—idf measures. Information Processing and Management: an International Journal 39(1), 45–65 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Carlo, A., Alberto, R.: Generalized thermodynamics underlying the laws of Zipf and Benford. In: Zhou, J. (ed.) Complex 2009. LNICST, vol. 5, pp. 2232–2237. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Fabio, C.: Spoken query processing for interactive information retrieval. Data & Knowledge Engineering 41, 105–124 (2002)CrossRefzbMATHGoogle Scholar
  4. 4.
    Pereira, F., Naftali, T., Lillian, L.: Distributional clustering of English words. In: Proceedings of the 31st Annual Meeting of the ACL, pp. 183–190 (1993)Google Scholar
  5. 5.
    Anette, H., Jussi, K., Anna, J.: Automatic Keyword Extraction Using Domain Knowledge. In: Gelbukh, A. (ed.) CICLing 2001. LNCS, vol. 2004, pp. 472–482. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  6. 6.
    Donald, M.: Generalized Inverse Document Frequency. In: Conference on Information and Knowledge Management, pp. 399–408 (2008)Google Scholar
  7. 7.
    Yataka, M., Mitsuru, I.: Keyword Extraction from a Single Document using Word Co-occurrence Statistical Information. International Journal on artificial Intelligence Tool 13(1), 157–169 (2004)CrossRefGoogle Scholar
  8. 8.
    Thomas, R.: A frequency-based and a Poisson-based definition of the probability of being informative. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 227–234 (2003)Google Scholar
  9. 9.
    Charles, S.: Dictionary of food: International Food and Cooking Terms from A to Z, 2nd edn. A&C Black Publishers Ltd. (2005)Google Scholar
  10. 10.
    Nobuyoshi, S., Minoru, U., Yoshifumi, S., Hideki, M.: Fresh Information Retrieval using Cooperative Meta Search Engines. In: Proceedings of the 16th International Conference on Information Networking (ICOIN-16), vol. 2, 7A-2, pp. 1–7 (2002)Google Scholar
  11. 11.
    O’Meara, T., Patel, A.: A topic-specific Web robot model based on restless bandits. IEEE Internet Computing 5(2), 27–35 (2001)CrossRefGoogle Scholar
  12. 12.
    Wim, V.: Agriculture and the food industry in the information age. European Review of Agricultural Economics 32(3), 347–368 (2005)CrossRefGoogle Scholar
  13. 13.
  14. 14.
  15. 15.
    CLucene Project web page, http://clucene.sourceforge.net/

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gwangbum Pyun
    • 1
  • Unil Yun
    • 1
  1. 1.Chungbuk National UniversityRepublic of Korea

Personalised recommendations