Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Processing Structural Constraints

  • Andrew Trotman
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_280

Definition

When searching unstructured plain text, the user is limited in the expressive power of their query – they can only ask for documents that are about something. When structure is present in the document, and with a query language that supports its use, the user is able to write far more precise queries. For example, searching for “smith” in a document is not necessarily equivalent to searching for “smith” as an author of a document. This increase in expressive power should lead to an increase in precision with no loss in recall. By specifying that “smith” should be the author, all those instances where “smith” was the profession will be dropped (increasing precision), while all those in which “smith” is the author will still be found (maintaining recall).

Historical Background

With the proliferation of structured and semi-structured markup languages such as SGML and XML came the possibility of unifying database and information retrieval technologies. The Evaluation of XML...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Trotman A, Lalmas M. Why structural hints in queries do not help XML retrieval. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; 2009. p. 711–2.Google Scholar
  2. 2.
    Woodley A, Geva S, Edwards SL. Comparing XML-IR query formation interfaces. Aust J Intell Inf Proc Syst. 2007;9(2):64–71.Google Scholar
  3. 3.
    van Zwol R, Baas J, van Oostendorp H, Wiering F. Bricks: the building blocks to tackle query formulation in structured document retrieval. In: Proceedings of the 28th European Conference on IR Research; 2006. p. 314–25.Google Scholar
  4. 4.
    Trotman A, Wang Q. Overview of the INEX 2010 data centric track. In: Proceedings of the 9th International Workshop of the Initiative for the Evaluation of XML Retrieval; 2010. p. 171–81.Google Scholar
  5. 5.
    Arvola P, Geva S, Kamps J, Schenkel R, Trotman A, Vainio J. Overview of the INEX 2010 ad hoc track. In: Proceedings of the 9th International Workshop of the Initiative for the Evaluation of XML Retrieval; 2010. p. 1–32.Google Scholar
  6. 6.
    Schuth A, Marx M. University of Amsterdam data centric ad hoc and faceted search runs. In: Proceedings of the 10th International Workshop of the Initiative for the Evaluation of XML Retrieval; 2011. p. 155–60.CrossRefGoogle Scholar
  7. 7.
    Wang Q, Gan Y, Sun Y. RUC @ INEX 2011 data-centric track. In: Proceedings of the 10th International Workshop of the Initiative for the Evaluation of XML Retrieval; 2011. p. 167–79.CrossRefGoogle Scholar
  8. 8.
    Trotman A, Lalmas M. Strict and vague interpretation of XML-retrieval queries. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; 2009. p. 709–10.Google Scholar
  9. 9.
    Mass Y, Mandelbrod M. Using the INEX environment as a test bed for various user models for XML retrieval. In: Proceedings of the 4th International Workshop of the Initiative for the Evaluation of XML Retrieval; 2005. p. 187–95.Google Scholar
  10. 10.
    Mihajlovic V, Ramírez G, Westerveld T, Hiemstra D, Blok HE, de Vries AP. Vtijah scratches INEX 2005: vague element selection, image search, overlap, and relevance feedback. In: Proceedings of the 4th International Workshop of the Initiative for the Evaluation of XML Retrieval; 2005. p. 72–87.Google Scholar
  11. 11.
    Doan A, Halevy AY. Semantic integration research in the database community: a brief survey. AI Mag. 2005;26(1):83–94.Google Scholar
  12. 12.
    van Zwol R. B3-sdr and effective use of structural hints. In: Proceedings of the 4th International Workshop of the Initiative for the Evaluation of XML Retrieval; 2005. p. 146–60.Google Scholar
  13. 13.
    Theobald M, Schenkel R, Weikum G. Topx and xxl at INEX 2005. In: Proceedings of the 4th International Workshop of the Initiative for the Evaluation of XML Retrieval; 2005. p. 282–95.Google Scholar
  14. 14.
    Hubert G. XML retrieval based on direct contribution of query components. In: Proceedings of the 4th International Workshop of the Initiative for the Evaluation of XML Retrieval; 2005. p. 172–86.Google Scholar
  15. 15.
    Sauvagnat K, Hlaoua L, Boughanem M. Xfirm at INEX 2005: ad-hoc and relevance feedback tracks. In: Proceedings of the 4th International Workshop of the Initiative for the Evaluation of XML Retrieval; 2005. p. 88–103.Google Scholar
  16. 16.
    O’Keefe RA If INEX is the answer, what is the question? In: Proceedings of the 3rd International Workshop of the Initiative for the Evaluation of XML Retrieval; 2004. p. 54–9.CrossRefGoogle Scholar
  17. 17.
    Trotman A. Wanted: element retrieval users. In: Proceedings of the 4th International Workshop of the Initiative for the Evaluation of XML Retrieval; 2005. p. 63–9.Google Scholar
  18. 18.
    Kamps J, Marx M, Rijke MD, Sigurbjörnsson B. Articulating information needs in XML query languages. Trans Inf Sys. 2006;24(4):407–36.CrossRefGoogle Scholar
  19. 19.
    Geva S. GPX – gardens point XML IR at INEX 2006. In: Proceedings of the 5th International Workshop of the Initiative for the Evaluation of XML Retrieval; 2006. p. 137–50.Google Scholar
  20. 20.
    Trotman A, Jia X-F, Crane M. Towards an efficient and effective search engine. In: Proceedings of the SIGIR 2012 Workshop on Open Source Information Retrieval; 2012. p. 40–7.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of OtagoDunedinNew Zealand

Section editors and affiliations

  • Jaap Kamps
    • 1
  1. 1.University of AmsterdamAmsterdamThe Netherlands