A Document Database Query Language

  • Nieves R. Brisaboa
  • Miguel R. Penabad
  • Ángeles S. Places
  • Francisco J. Rodríguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2405)


This work presents a natural language based technique to build user interfaces to query document databases through the web. We call such technique Bounded Natural Language (BNL). Interfaces based on BNL are useful to query document databases containing only structured data, containing only text or containing both of them. That is, the underlying formalism of BNL can integrate restrictions over structured and non-structured data (as text).

Interfaces using BNL can be programmed ad hoc for any document database but in this paper we present a system with an ontology based architecture in which the user interface is automatically generated by a software module (User Interface Generator) capable of reading and following the ontology. This ontology is a conceptualization of the database model, which uses a label in natural language for any concept in the ontology. Each label represents the usual name for a concept in the real world.

The ontology includes general concepts useful when the user is interested in documents in any corpus in the database, and specific concepts useful when the user is interested in a specific corpus. That is, databases can store one or more corpus of documents and queries can be issued either over the whole database or over a specific corpus.

The ontology guides the execution of the User Interface Generator and other software modules in such a way that any change in the database does not imply making changes in the program code, because the whole system runs following the ontology. That is, if a modification in the database schema occurs, only the ontology must be changed and the User Interface Generator will produce a new and different user interface adapted to the new database.


Database Schema Text Retrieval Query User Interface Query System Natural Language Sentence 
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.


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  1. 1.
    Askjeeves 2001
  2. 2.
    Alonso, O. and Baeza-Yates, R. “A model and software architecture for search results visualization on the WWW”, Proceedings of the International Symposium on String Processing and Information Retrieval SPIRE 2000, IEEE Computer Society Press, A Coruña Spain, September 27–29, 2000, pp:8–15.CrossRefGoogle Scholar
  3. 3.
    Baeza-Yates, R.; Navarro, G. Integrating contents and structure in text retrieval. ACM SIG-MOD Record, 25(1):67–79, Marzo 1996.Google Scholar
  4. 4.
    Baeza-Yates, R.; Navarro, G.; Vegas, J.; Fuente, P. A model and a visual query language for structured text. En Berthier Ribeiro-Neto (Eds.) Proc. of the 5th Symposium on String Processing and Information Retrieval, pp:7–13, Santa Cruz, Bolivia, Sept 1998. IEEE CS Press.Google Scholar
  5. 5.
    Baeza-Yates, R.; Ribeiro-Neto, B. Modern Information Retrieval, Addison-Wesley, 1999.Google Scholar
  6. 6.
    Berry, M. W.; Dumais, S. T.; O’Brien, W. Using Linear Algebra for Intelligent Information Retrieval. SIAM Review 37:573–595, 1995.zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Cousins, Steve B., Paepcke, Andreas, Winograd, Terry, Eric A. Bier and Ken Pier; The digital library integrated task environment (DLITE); Proceedings of the 2nd ACM international conference on Digital libraries, 1997, Pages 142–151Google Scholar
  8. 8.
    Gruber, T. Toward Principles for the Design of Ontologies Used for Knowledge Sharing. IJHCS, 43(5/6): 907–928. 1994.Google Scholar
  9. 9.
  10. 10.
    Guarino, N. (ed.), Formal Ontology in Information Systems. Proceedings of FOIS’98. Amsterdam, IOS Press, pp. 3–15., Trento, Italy, 6–8 June 1998.Google Scholar
  11. 11.
    Hearst, Marti A. and Chandu Karadi; Cat-a-Cone: An interactive interface for specifying searches and viewing retrieval results using a large category hierarchy; Proceedings of the 20th annual international ACM SIGIR. Conference on Research and development in information retrieval, 1997, Pages 246–255.Google Scholar
  12. 12.
    Hearst, M. “User interfaces and visualization” in Modern Information Retrieval, Addison-Wesley, London, 1999Google Scholar
  13. 13.
    Koenemann, Juergen and Belkin, Nicholas (1996). A case for interaction: A study of interactive information retrieval behavior and effectiveness. Proc. CHI’96 Human Factors in Computing Systems, ACM Press, New York, NY, pp. 205–212.Google Scholar
  14. 14.
    Landauer, T., Egan, D., Remde, J., Lesk, M., Lochbaum, C., and Ketchum, D. Enhancing the usability of text through computer delivery and formative evaluation: The SuperBook project. Hypertext-A Psychological Perspective. Ellis Horwood, 1993.Google Scholar
  15. 15.
    Mena, E., Illarramendi, A., Kashyap, V., Sheth, A. OBSERVER: An Approach for Query Processing in Global Information Systems based on Interoperation across Pre-existing Ontologies. Journal Distributed And Parallel Databases (DAPD). 1998.Google Scholar
  16. 16.
    Ogawa, Y.; Morita, T.; Kobayashi, K. A fuzzy document retrieval system using the keyword connection matrix and a learning method. Fuzzy Sets and Systems, 39:163–179, 1991.CrossRefMathSciNetGoogle Scholar
  17. 17.
    Penabad, M., Durán, M.J., Lalín, C, López, J.R., Paramá, J, Places, A. S. y Brisaboa, N.R. Using Bounded Natural Language to Query Databases on the Web. Proceeding of the Information Systems, Analysis and Synthesis ISAS’99. Orlando (Florida), Julio-Agosto 1999.Google Scholar
  18. 18.
    Rao, Ramana, Card, Stuart K., Jellinek, Herbert D., Mackinlay, Jock D. and Robertson, George G. The information grid: A framework for information retrieval and retrieval-centered applications. Proceedings of the fifth annual ACM symposium on User interface software and technology, 1992, Pages 23–32Google Scholar
  19. 19.
    Rijsbergen, C.J. van. Information Retrieval. Butterworths, 1979.Google Scholar
  20. 20.
    Robertson, G. C.; Sparck Jones, K. Relevance weighting of search terms. Journal of the American Society for Information Sciences, 27(3):129–146, 1976.CrossRefGoogle Scholar
  21. 21.
    Salton, G. Automatic information Organization and Retrieval. McGraw-hill, 1968.Google Scholar
  22. 22.
    Salton, G. The SMART Retrieval System-Experiments in Automatic Document Processing. Prentice Hall Inc., Englewood Cliffs, NJ, 1971.Google Scholar
  23. 23.
    Salton, G.; Fox, E. A.; Wu, H. Extended Boolean information retrieval. Communications of the ACM, 26(11):1022–1036, November 1983.Google Scholar
  24. 24.
    Salton, G., and Buckley, C. 1990. Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science 41:288–297.CrossRefGoogle Scholar
  25. 25.
    Shneiderman. B. “Designing the User Interface: Strategies for Effective Human-Computer Interaction”, 3ed, Massachusetts Addison-Wesley, 1998.Google Scholar
  26. 26.
    Strzalkowski, Tomek, editor. Natural Language Information Retrieval. Kluwer Academic Publishers, Dordrecht, April 1999zbMATHGoogle Scholar
  27. 27.
    Vegas, J. “Un sistema de recuperación de información sobre estructura y contenido”, Tesis doctoral. Universidad de Valladolid, Valladolid, Spain, 1999.Google Scholar
  28. 28.
    Wong, S. K. M.; Ziarko, W.; Wong, P. C. N. Generalized vector space model in information retrieval. Proc. 8th ACM SIGIR Conference on Research and Development in information Retrieval, pp:18–25, New Yok, USA, 1985.Google Scholar
  29. 29.
    World Wide Web Consortium. Standard XGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Nieves R. Brisaboa
    • 1
  • Miguel R. Penabad
    • 1
  • Ángeles S. Places
    • 1
  • Francisco J. Rodríguez
    • 1
  1. 1.Dep. de ComputaciónUniv. de A CoruñaA CoruñaEspaña

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