TY - JOUR
T1 - QUEST
T2 - A keyword search system for relational data based on semantic and machine learning techniques
AU - Bergamaschi, Sonia
AU - Guerra, Francesco
AU - Interlandi, Matteo
AU - Trillo-Lado, Raquel
AU - Velegrakis, Yannis
PY - 2013/1/1
Y1 - 2013/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84891070608&partnerID=8YFLogxK
U2 - 10.14778/2536274.2536281
DO - 10.14778/2536274.2536281
M3 - Article
AN - SCOPUS:84891070608
SN - 2150-8097
VL - 6
SP - 1222
EP - 1225
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
IS - 12
ER -