QUEST: a keyword search system for relational data based on semantic and machine learning techniques

Authors:
Sonia Bergamaschi;Francesco Guerra;Matteo Interlandi;Raquel Trillo-Lado;Yannis Velegrakis
Affiliations:
University of Modena and Reggio Emilia, Italy;University of Modena and Reggio Emilia, Italy;University of Modena and Reggio Emilia, Italy;University of Zaragoza, Spain;University of Trento, Italy
Venue:
Proceedings of the VLDB Endowment
Year:
2013

Citing 4
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Abstract

We showcase QUEST (QUEry generator for STructured sources), a search engine for relational databases that combines semantic and machine learning techniques for transforming keyword queries into meaningful SQL queries. The search engine relies on two approaches: the forward, providing mappings of keywords into database terms (names of tables and attributes, and domains of attributes), and the backward, computing the paths joining the data structures identified in the forward step. The results provided by the two approaches are combined within a probabilistic framework based on the Dempster-Shafer Theory. We demonstrate QUEST capabilities, and we show how, thanks to the flexibility obtained by the probabilistic combination of different techniques, QUEST is able to compute high quality results even with few training data and/or with hidden data sources such as those found in the Deep Web.