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QUEST: A Keyword Search System for Relational Data based on Semantic and Machine Learning Techniques

Summary: QUEST combines semantic and ML to translate keyword queries into SQL over relational data. Forward mappings to terms and a backward path-join are fused by Dempster-Shafer theory, delivering robust results with little training data and hidden data sources (Deep Web). (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10766
Venue
VLDB
Year
2013
Pagerank
9.479259e-05
Overall Rank
1,922 | 86.82%
DOI
10.14778/2536274.2536281

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{bergamaschi_vldb13,
        title = {{QUEST: A Keyword Search System for Relational Data based on Semantic and Machine Learning Techniques}},
        author = {Bergamaschi, Sonia and Guerra, Francesco and Interlandi, Matteo and Trillo-Lado, Raquel and Velegrakis, Yannis},
        journal = {PVLDB},
        series = {{VLDB} '13},
        volume = {6},
        number = {12},
        pages = {1222--1225},
        doi = {10.14778/2536274.2536281},
        url = {https://doi.org/10.14778/2536274.2536281},
        year = {2013}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
3,572 Summary Graphs for Relational Database Schemas 2011 VLDB 7.2980153e-05
3,651 Keyword Search over Relational Databases: A Metadata Approach 2011 SIGMOD 7.2249652e-05
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