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

Sonia Bergamaschi, Francesco Guerra, Matteo Interlandi, Raquel Trillo-Lado, Yannis Velegrakis

Research output: Contribution to journalArticle

27 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1222-1225
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number12
Publication statusPublished - Aug 2013


ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

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