Toward Approximate GML Retrieval Based on Structural and Semantic Characteristics

  • Joe Tekli
  • Richard Chbeir
  • Fernando Ferri
  • Patrizia Grifoni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6189)

Abstract

GML is emerging as the new standard for representing geographic information in GISs on the Web, allowing the encoding of structurally and semantically rich geographic data in self describing XML-based geographic entities. In this study, we address the problem of approximate querying and ranked results for GML data and provide a method for GML query evaluation. Our method consists of two main contributions. First, we propose a tree model for representing GML queries and data collections. Then, we introduce a GML retrieval method based on the concept of tree edit distance as an efficient means for comparing semi-structured data. Our approach allows the evaluation of both structural and semantic similarities in GML data, enabling the user to tune the querying process according to her needs. The user can also choose to perform either template querying, taking into account all elements in the query and data trees, or minimal constraint querying, considering only those elements required by the query (disregarding additional data elements), in the similarity evaluation process. An experimental prototype was implemented to test and validate our method. Results are promising.

Keywords

GML Search Ranked Retrieval Structural & Semantic Similarity GIS 

References

  1. 1.
    Amer-Yahia, S., Lakshmanan, L., Pandit, S.: FleXPath: Flexible Structure and Full-Text Querying for XML. In: Proc. of the ACM Inter. Conf. on Management of Data (SIGMOD), pp. 83–94 (2004)Google Scholar
  2. 2.
    Bar-Ilan, J.: Comparing rankings of search results on the Web. Information Processessing and Management (41), 1511–1519 (2005)Google Scholar
  3. 3.
    Chawathe, S.: Comparing Hierarchical Data in External Memory. In: Proceedings of VLDB, pp. 90–101 (1999)Google Scholar
  4. 4.
    Dalamagas, T., et al.: A Methodology for Clustering XML Documents by Structure. Information Systems 31(3), 187–228 (2006)CrossRefGoogle Scholar
  5. 5.
    Ferri, F., Grifoni, P., Rafanelli, M.: The Management of Spatial and Temporal Constraints in GIS using Pictorial Interaction on the Web. In: Persson, A., Stirna, J. (eds.) CAiSE 2004. LNCS, vol. 3084, pp. 92–105. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Fuhr, N., Großjohann, K.: XIRQL: A Query Language for Information Retrieval. In: Proc. of the ACM-SIGIR Conference, pp. 172–180 (2001)Google Scholar
  7. 7.
    Grabs, T., Schek, H.-J.: Generating Vector Spaces On-the-fly for Flexible XML Retrieval. In: Proc. of ACM SIGIR Workshop on XML and Information Retrieval, pp. 4–13 (2002)Google Scholar
  8. 8.
    Guo, L., et al.: XRANK: ranked keyword search over XML documents. ACM SIGMOD, 16–27 (2003)Google Scholar
  9. 9.
    Hammiche, S., et al.: Semantic Retrieval of Multimedia Data. In: ACM MMDB Workshop, pp. 36–44 (2004)Google Scholar
  10. 10.
    Jones, C., Purves, R.: Geographic Information Retrieval. J. of Geo. Info. Science 22(3), 219–228 (2008)CrossRefGoogle Scholar
  11. 11.
    Larson, R.: Geographic Information Retrieval and Spatial Browsing. In: GIS and Libraries: Patrons Maps and Spatial Information, pp. 81–124 (1996)Google Scholar
  12. 12.
    Lin, D.: An Information-Theoretic Definition of Similarity. In: Proc. of the ICML Conference, pp. 296–304 (1998)Google Scholar
  13. 13.
    Maguitman, A., et al.: Algorithmic Detection of Semantic Similarity. In: WWW Conference, pp. 107–116 (2005)Google Scholar
  14. 14.
    Marian, A., et al.: Adaptive Processing of Top-k Queries in XML. In: ICDE Conference, pp. 162–173 (2005)Google Scholar
  15. 15.
    Miller, G.: WordNet: An On-Line Lexical Database. International Journal of Lexicography 3(4) (1990)Google Scholar
  16. 16.
    Motro, A.: Vague: A User Interface to Relational Databases that Permits Vague Queries. ACM Transactions on Office Information Systems 6(3), 187–214 (1988)CrossRefGoogle Scholar
  17. 17.
    Nierman, A., Jagadish, H.V.: Evaluating structural similarity in XML documents. In: Proc. of the ACM WebDB Workshop, pp. 61–66 (2002)Google Scholar
  18. 18.
    Open Geospatial Consortium. Geography Mark-up Language, http://www.opengeospatial.org/standards/gml
  19. 19.
    Paul, M., Gosh, S.K.: An Approach for Geospatial Data Management for Efficient Web Retrieval. In: Proc. of the 6th IEEE International Conference on Computer and Information Technology (2006)Google Scholar
  20. 20.
    Pokorny, J., Rejlek, V.: Databases and Info. Systems, Frontiers in Artificial Intelligence and Applications. In: Barzdins, J., Caplinskas, A. (eds.) A Matrix Model for XML Data, pp. 53–64. IOS Press, Amsterdam (2005)Google Scholar
  21. 21.
    Rodriguez, M.A., Egenhofer, M.J.: Comparing Geospatial Entity Classes: an Asymmetric and Content-Dependent Similarity Measure. Journal of Geographical Information Science 18(3), 229–256 (2004)CrossRefGoogle Scholar
  22. 22.
    Salton, G.: The SMART Retrieval System. Prentice Hall, New Jersey (1971)Google Scholar
  23. 23.
    Schlieder, T.: Similarity Search in XML Data Using Cost-based Query Transformations. In: Proc. of the International ACM WebDB Workshop, pp. 19–24 (2001)Google Scholar
  24. 24.
    Schlieder, T., Meuss, H.: Querying and Ranking XML Documents. Journal of the American Society for Information Science, Special Topic XML/IR 53(6), 489–503 (2002)CrossRefGoogle Scholar
  25. 25.
    Tekli, J., Chbeir, R., Yetongnon, K.: Extensible User-based Grammar Matching. In: ER Conf., pp. 294–314 (2009)Google Scholar
  26. 26.
    Tekli, J., Chbeir, R., Yetongnon, K.: Efficient XML Structural Similarity Detection using Sub-tree Commonalities. In: Brazilian Symposium on Databases (SBBD) and SIGMOD DiSC, pp. 116–130 (2007)Google Scholar
  27. 27.
    Torres, M., et al.: Retrieving Geospatial Information into a Web-Mapping Application using Geospatial Ontologies. In: Nguyen, N.T., Grzech, A., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2007. LNCS (LNAI), vol. 4496, pp. 267–277. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  28. 28.
    World Wide Web Consortium. The Document Object Model (DOM) (May 2009), http://www.w3.org/DOM
  29. 29.
    Wu, Z., Palmer, M.: Verb Semantics and Lexical Selection. In: Proc. of the 32nd Annual Meeting of the Associations of Computational Linguistics, pp. 133–138 (1994)Google Scholar
  30. 30.
    Zhang, Z., Li, R., Cao, S., Zhu, Y.: Similarity Metric in XML Documents. In: Knowledge Management and Experience Management Workshop (2003)Google Scholar
  31. 31.
    Zhu, F., Guan, J., Zhou, J., Zhou, S.: Storing and Querying GML in Object-Relational Databases. In: Proc. of the 14th Annual ACM Inter. Symp. on Advances in Geographic Information Systems, pp. 107–114 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Joe Tekli
    • 1
  • Richard Chbeir
    • 1
  • Fernando Ferri
    • 2
  • Patrizia Grifoni
    • 2
  1. 1.LE2I Laboratory UMR-CNRSUniversity of BourgogneDijon CedexFrance
  2. 2.IRPPS-CNRRomaItaly

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