Advertisement

XML Information Retrieval through Tree Edit Distance and Structural Summaries

  • Cyril Laitang
  • Mohand Boughanem
  • Karen Pinel-Sauvagnat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7097)

Abstract

Semi-structured Information Retrieval (SIR) allows the user to narrow his search down to the element level. As queries and XML documents can be seen as hierarchically nested elements, we consider that their structural proximity can be evaluated through their trees similarity. Our approach combines both content and structure scores, the latter being based on tree edit distance (minimal cost of operations to turn one tree to another). We use the tree structure to propagate and combine both measures. Moreover, to overcome time and space complexity, we summarize the document tree structure. We experimented various tree summary techniques as well as our original model using the SSCAS task of the INEX 2005 campaign. Results showed that our approach outperforms state of the art ones.

Keywords

Edit Distance Vector Space Model Tree Match Virtual Edge Structure Score 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alilaouar, A., Sedes, F.: Fuzzy querying of XML documents. In: International Conference on Web Intelligence and Intelligent Agent Technology, Compigne, France, pp. 11–14. IEEE/WIC/ACM (September 2005)Google Scholar
  2. 2.
    Ben Aouicha, M., Tmar, M., Boughanem, M.: Flexible document-query matching based on a probabilistic content and structure score combination. In: Symposium on Applied Computing (SAC), Sierre, Switzerland. ACM (March 2010)Google Scholar
  3. 3.
    Boobna, U., de Rougemont, M.: Correctors for XML Data. In: Bellahsène, Z., Milo, T., Rys, M., Suciu, D., Unland, R. (eds.) XSym 2004. LNCS, vol. 3186, pp. 97–111. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Dalamagas, T., Cheng, T., Winkel, K.-J., Sellis, T.: A methodology for clustering XML documents by structure. Information Systems 31, 187–228 (2006)CrossRefMATHGoogle Scholar
  5. 5.
    Damiani, E., Tanca, L., Arcelli, F.: Fuzzy XML queries via context-based choice of aggregation. Kybernetika 36, 635–655 (2000)MATHGoogle Scholar
  6. 6.
    Demaine, E.D., Mozes, S., Rossman, B., Weimann, O.: An optimal decomposition algorithm for tree edit distance. ACM Trans. Algorithms 6, 2:1–2:19 (2009)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Demartin, G., Denoyer, L., et al.: Report on INEX 2008. SIGIR Forum 43, 17–36 (2009)Google Scholar
  8. 8.
    Dulucq, S., Touzet, H.: Analysis of tree edit distance algorithms. In: Proceedings of the 14th Annual Symposium of Combinatorial Pattern Matching, pp. 83–95 (2003)Google Scholar
  9. 9.
    Geva, S., Kamps, J., Lethonen, M., Schenkel, R., Thom, J.A., Trotman, A.: Overview of the INEX 2009 Ad Hoc Track. In: Geva, S., Kamps, J., Trotman, A. (eds.) INEX 2009. LNCS, vol. 6203, pp. 4–25. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Hassler, M., Bouchachia, A.: Searching XML Documents – Preliminary Work. In: Fuhr, N., Lalmas, M., Malik, S., Kazai, G. (eds.) INEX 2005. LNCS, vol. 3977, pp. 119–133. Springer, Heidelberg (2006)Google Scholar
  11. 11.
    Sparck Jones, K.: Index term weighting. Information Storage and Retrieval 9(11), 619–633 (1973)CrossRefGoogle Scholar
  12. 12.
    Kazai, G., Lalmas, M.: INEX 2005 Evaluation Measures. In: Fuhr, N., Lalmas, M., Malik, S., Kazai, G. (eds.) INEX 2005. LNCS, vol. 3977, pp. 16–29. Springer, Heidelberg (2006)Google Scholar
  13. 13.
    Klein, P.N.: Computing the Edit-Distance Between Unrooted Ordered Trees. In: Bilardi, G., Pietracaprina, A., Italiano, G.F., Pucci, G. (eds.) ESA 1998. LNCS, vol. 1461, pp. 91–102. Springer, Heidelberg (1998)Google Scholar
  14. 14.
    Levenshtein, V.I.: Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Soviet Physics Doklady 10, 707 (1966)MathSciNetMATHGoogle Scholar
  15. 15.
    Mass, Y., Mandelbrod, M.: Component Ranking and Automatic query Refinement for XML Retrieval. In: Fuhr, N., Lalmas, M., Malik, S., Szlávik, Z. (eds.) INEX 2004. LNCS, vol. 3493, pp. 73–84. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  16. 16.
    Popovici, E., Ménier, G., Marteau, P.-F.: SIRIUS: A Lightweight XML Indexing and Approximate Search System at INEX 2005. In: Fuhr, N., Lalmas, M., Malik, S., Kazai, G. (eds.) INEX 2005. LNCS, vol. 3977, pp. 321–335. Springer, Heidelberg (2006)Google Scholar
  17. 17.
    Rougemont, M., Vieilleribière, A.: Approximate schemas, source-consistency and query answering. J. Intell. Inf. Syst. 31, 127–146 (2008)CrossRefGoogle Scholar
  18. 18.
    Tai, K.-C.: The tree-to-tree correction problem. J. ACM 26, 422–433 (1979)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Theobald, M., Schenkel, R., Weikum, G.: TopX and XXL at INEX 2005. In: Fuhr, N., Lalmas, M., Malik, S., Kazai, G. (eds.) INEX 2005. LNCS, vol. 3977, pp. 282–295. Springer, Heidelberg (2006)Google Scholar
  20. 20.
    Trotman, A., Sigurbjörnsson, B.: Narrowed Extended XPath I (NEXI). In: Fuhr, N., Lalmas, M., Malik, S., Szlávik, Z. (eds.) INEX 2004. LNCS, vol. 3493, pp. 16–40. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Cyril Laitang
    • 1
  • Mohand Boughanem
    • 1
  • Karen Pinel-Sauvagnat
    • 1
  1. 1.IRIT-SIGToulouseFrance

Personalised recommendations