KEYRY: a keyword-based search engine over relational databases based on a hidden Markov model

Authors:
Sonia Bergamaschi;Francesco Guerra;Silvia Rota;Yannis Velegrakis
Affiliations:
Università di Modena e Reggio Emilia, Italy;Università di Modena e Reggio Emilia, Italy;Università di Modena e Reggio Emilia, Italy;University of Trento, Italy
Venue:
ER'11 Proceedings of the 30th international conference on Advances in conceptual modeling: recent developments and new directions
Year:
2011

Citing 2
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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.