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

Sonia Bergamaschi; Francesco Guerra; Silvia Rota; Yannis Velegrakis

doi:10.1007/978-3-642-24574-9_42

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

Sonia Bergamaschi^*, Francesco Guerra, Silvia Rota, Yannis Velegrakis

^*Corresponding author for this work

Sub Data Intensive Systems

University of Modena and Reggio Emilia

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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.

Original language	English
Title of host publication	Advances in Conceptual Modeling
Subtitle of host publication	Recent Developments and New Directions - ER 2011 Workshops FP-UML, MoRE-BI, Onto-CoM, SeCoGIS, Variability@ER, WISM, Proceedings
Pages	328-331
Number of pages	4
DOIs	https://doi.org/10.1007/978-3-642-24574-9_42
Publication status	Published - 17 Nov 2011
Event	30th International Conference on Conceptual Modeling, ER 2011 - Brussels, Belgium Duration: 31 Oct 2011 → 3 Nov 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6999 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	30th International Conference on Conceptual Modeling, ER 2011
Country/Territory	Belgium
City	Brussels
Period	31/10/11 → 3/11/11

Access to Document

10.1007/978-3-642-24574-9_42

Cite this

Bergamaschi, S., Guerra, F., Rota, S., & Velegrakis, Y. (2011). KEYRY: A keyword-based search engine over relational databases based on a Hidden Markov model. In Advances in Conceptual Modeling: Recent Developments and New Directions - ER 2011 Workshops FP-UML, MoRE-BI, Onto-CoM, SeCoGIS, Variability@ER, WISM, Proceedings (pp. 328-331). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6999 LNCS). https://doi.org/10.1007/978-3-642-24574-9_42

Bergamaschi, Sonia ; Guerra, Francesco ; Rota, Silvia et al. / KEYRY : A keyword-based search engine over relational databases based on a Hidden Markov model. Advances in Conceptual Modeling: Recent Developments and New Directions - ER 2011 Workshops FP-UML, MoRE-BI, Onto-CoM, SeCoGIS, Variability@ER, WISM, Proceedings. 2011. pp. 328-331 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "KEYRY: A keyword-based search engine over relational databases based on a Hidden Markov model",

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.",

author = "Sonia Bergamaschi and Francesco Guerra and Silvia Rota and Yannis Velegrakis",

year = "2011",

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doi = "10.1007/978-3-642-24574-9_42",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Bergamaschi, S, Guerra, F, Rota, S & Velegrakis, Y 2011, KEYRY: A keyword-based search engine over relational databases based on a Hidden Markov model. in Advances in Conceptual Modeling: Recent Developments and New Directions - ER 2011 Workshops FP-UML, MoRE-BI, Onto-CoM, SeCoGIS, Variability@ER, WISM, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6999 LNCS, pp. 328-331, 30th International Conference on Conceptual Modeling, ER 2011, Brussels, Belgium, 31/10/11. https://doi.org/10.1007/978-3-642-24574-9_42

KEYRY: A keyword-based search engine over relational databases based on a Hidden Markov model. / Bergamaschi, Sonia; Guerra, Francesco; Rota, Silvia et al.
Advances in Conceptual Modeling: Recent Developments and New Directions - ER 2011 Workshops FP-UML, MoRE-BI, Onto-CoM, SeCoGIS, Variability@ER, WISM, Proceedings. 2011. p. 328-331 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6999 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AB - 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.

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Bergamaschi S, Guerra F, Rota S, Velegrakis Y. KEYRY: A keyword-based search engine over relational databases based on a Hidden Markov model. In Advances in Conceptual Modeling: Recent Developments and New Directions - ER 2011 Workshops FP-UML, MoRE-BI, Onto-CoM, SeCoGIS, Variability@ER, WISM, Proceedings. 2011. p. 328-331. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-24574-9_42

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

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