KEYRY: A Keyword-Based Search Engine over Relational Databases Based on a Hidden Markov Model

  • Sonia Bergamaschi
  • Francesco Guerra
  • Silvia Rota
  • Yannis Velegrakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6999)

Abstract

We propose the demonstration of KEYRY, a tool for translating keyword queries over structured data sources into queries in the native language of the data source. KEYRY does not assume any prior knowledge of the source contents. This allows it to be used in situations where traditional keyword search techniques over structured data that require such a knowledge cannot be applied, i.e., sources on the hidden web or those behind wrappers in integration systems. In KEYRY the search process is modeled as a Hidden Markov Model and the List Viterbi algorithm is applied to computing the top-k queries that better represent the intended meaning of a user keyword query. We demonstrate the tool’s capabilities, and we show how the tool is able to improve its behavior over time by exploiting implicit user feedback provided through the selection among the top-k solutions generated.

Keywords

Hiden Markov Model Relational Database Intended Meaning Keyword Query Keyword Matcher 
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.
    Bergamaschi, S., Domnori, E., Guerra, F., Lado, R.T., Velegrakis, Y.: Keyword search over relational databases: a metadata approach. In: Sellis, T.K., Miller, R.J., Kementsietsidis, A., Velegrakis, Y. (eds.) SIGMOD Conference, pp. 565–576. ACM, New York (2011)Google Scholar
  2. 2.
    Bergamaschi, S., Guerra, F., Rota, S., Velegrakis, Y.: A Hidden Markov Model Approach to Keyword-based Search over Relational Databases. In: De Troyer, O., et al. (eds.) ER 2011 Workshops. LNCS, vol. 6999, pp. 328–331. Springer, Heidelberg (2011)Google Scholar
  3. 3.
    Bourgeois, F., Lassalle, J.-C.: An extension of the Munkres algorithm for the assignment problem to rectangular matrices. Communications of ACM 14(12), 802–804 (1971)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Li, L., Shang, Y., Shi, H., Zhang, W.: Performance evaluation of hits-based algorithms. In: Hamza, M.H. (ed.) Communications, Internet, and Information Technology, pp. 171–176. IASTED/ACTA Press (2002)Google Scholar
  5. 5.
    Seshadri, N., Sundberg, C.-E.: List Viterbi decoding algorithms with applications. IEEE Transactions on Communications 42(234), 313–323 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sonia Bergamaschi
    • 1
  • Francesco Guerra
    • 1
  • Silvia Rota
    • 1
  • Yannis Velegrakis
    • 2
  1. 1.Università di Modena e Reggio EmiliaItaly
  2. 2.University of TrentoItaly

Personalised recommendations