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A Flexible Weighting Scheme for Multimedia Documents

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1677))

Abstract

In information retrieval systems, it is common practice to rank the retrieved documents in decreasing order of their estimated relevance to the user’s query. Information retrieval models, such as the vector-space model (see Salton’s work), provide weighting schemes and matching functions that follow this necessity. However, they were mainly developed in the context of textual document retrieval. The contribution of this paper is twofold. Firstly, it takes a look at the challenges involved in the ordering of the results in image retrieval, while using the expressive conceptual graphs formalism as the indexing language. New parameters appear to be useful in the vector-space weighting schemes, that take into account the richness and complexity of documents such as images. We inspect such parameters and give a flexible weighting scheme. Secondly, this paper gives a general weighting scheme, applied for the conceptual graphs formalism. The matching function of this formalism, which otherwise gives only a boolean yes or no decision on a document’s relevance to a user’s query, is refined so that to obtain ranked results.

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References

  1. G. Salton and M.J. McGill. Introduction to Modern Information Retrieval. Mcgraw Hill Book Company, New York, 1983.

    MATH  Google Scholar 

  2. C.J. van Rijsbergen. Information Retrieval. Butterworths, London, 1979.

    Google Scholar 

  3. K. Sparck Jones. A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28(3):289–299, 1972.

    Google Scholar 

  4. G. Salton. Automatic Text Processing: the transformation, analysis, and retrieval of information by computer. Addison-Wesley Publishing Company, 1989.

    Google Scholar 

  5. G. Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5):513–523, 1988.

    Article  Google Scholar 

  6. C. Buckley, J. Allan, and G. Salton. Automatic routing and retrieval using SMART: TREC-2. Information Processing & Management, 31(3):315–326, 1995.

    Article  Google Scholar 

  7. C. Meghini, F. Sebastiani, U. Straccia, and C. Thanos.Amodel of information retrieval based on a terminological logic. In R. Korfhage, E. Rassmussen, and P. Willet, editors, Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Pittsburgh, PA, pages 298–307.ACM, ACM Press, June 1993.

    Google Scholar 

  8. I. Ounis. Un mod`ele d’indexation relationnel pour les graphes conceptuels fondé sur une interprétation logique. PhD thesis, Université Joseph Fourier, Grenoble, February 1998.

    Google Scholar 

  9. T.W.C. Huibers, I. Ounis, and J.P. Chevallet. Conceptual graphs aboutness. In P.W. Eklund, G. Ellis, and G. Mann, editors, Proceedings of the 4th International Conference on Conceptual Structures, ICCS’96, volume 1115 of Lecture Notes in Artificial Intelligence, pages 130–144, Sydney, August 1996. Springer-Verlag, Berlin.

    Google Scholar 

  10. J.P. Callan, W.B. Croft, and J. Broglio. TREC and TIPSTER experiments with INQUERY. Information Processing & Management, 31(3):327–343, 1995.

    Article  Google Scholar 

  11. I. Ounis and T.W.C. Huibers. A logical relational approach for information retrieval indexing. In 19th Annual BCS-IRSG Colloquium on IR Research, Aberdeen, Scotland. EWIC, Springer-Verlag, 8-9 April 1997.

    Google Scholar 

  12. J. Farradane. Relational indexing Part I. Journal of Information Science, 1(5):267–276, 1980.

    Article  Google Scholar 

  13. I. Ounis and M. Pasca. RELIEF: Combining expressiveness and rapidity into a single system. In The ACM SIGIR’98 conference,Melbourne, Australia, 1998.

    Google Scholar 

  14. G. Salton, C.S. Yang, and C.T. Yu. A theory of term importance in automatic text analysis. Journal of the ASIS, 26(1):33–44, 1975.

    Google Scholar 

  15. A. Singhal, G. Salton, M. Mitra, and C. Buckley. Document length normalization. Information Processing & Management, 32(5):619–633, 1996.

    Article  Google Scholar 

  16. O. Andrieu. Trouver l’info sur l’internet. Eyrolles, Paris, France, 1998.

    Google Scholar 

  17. J. May and P. Barnard. Modelling multimodal interaction: A theory-based technique for design analysis and support. In Human-Computer Interaction (INTERACT’ 97), pages 667–668, London. Also in http://www.shef.ac.uk/ pc1jm/guide.html, 1997. Chapman & Hall.

  18. A. Plante, S. Tanaka, and S. Inoue. Evaluating the location of hot spots in interactive scenes using the 3R toolbox. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI’98), pages 117–123, Los Angeles, USA, 1998. ACM Press.

    Google Scholar 

  19. J.F. Sowa. Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley Publishing Company, 1984.

    Google Scholar 

  20. H. van den Berg. Knowledge Graphs and Logic. PhD thesis, September 1993.

    Google Scholar 

  21. P.E. Maher. A similarity measure for conceptual graphs. International Journal of Intelligent Systems, 8:819–837, 1993.

    Article  Google Scholar 

  22. V. Wuwongse and M. Manzano. Fuzzy conceptual graphs. In In ICCS’ 93 International Conference on Conceptual Structures,Quebec City, Canada, pages 430–449, August 1993.

    Google Scholar 

  23. C. J. van Rijsbergen. A new theoretical framework for information retrieval. In ACM Conference on Research and development in Information Retrieval, Pisa, pages 194–200, 1986.

    Google Scholar 

  24. M. Mechkour. EMIR2. An extended model for image representation and retrieval. In DEXA’95. Database and EXpert system Applications, London., pages 395–404, September 1995.

    Google Scholar 

  25. I. Ounis and J.P. Chevallet. Using conceptual graphs in a multifaceted logical model for information retrieval. In R.R. Wagner and H. Thoma, editors, Proceedings of the 7th International Conference on Database and EXpert Systems Applications, DEXA’96, volume 1134 of Lecture Notes in Computer Science, pages 812–823, Zurich, Switzerland, September 1996. Springer.

    Google Scholar 

  26. F. Paradis. Un mod`ele d’indexation pour les documents textuels structurés. PhD thesis, Université Joseph Fourier, Novembre 1996.

    Google Scholar 

  27. Y. Chiaramella and M. Mechkour. Indexing an image test collection. Technical report, FERMI BRA 8134, April 1997.

    Google Scholar 

  28. J. May, S. Scott, and P. Barnard. Structuring Displays: a psychological guide. Eurographics Tutorial Notes Series, EACG: Geneva, 1995.

    Google Scholar 

  29. I. Ounis and M. Pasca. Finding the best parameters for image ranking: a user-oriented approach. In The IEEE Knowledge and Data Engineering Exchange Workshop (KDEX-98), Taipei, Taiwan, November, 1998.

    Google Scholar 

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Ounis, I. (1999). A Flexible Weighting Scheme for Multimedia Documents. In: Bench-Capon, T.J., Soda, G., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 1999. Lecture Notes in Computer Science, vol 1677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48309-8_37

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  • DOI: https://doi.org/10.1007/3-540-48309-8_37

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