Generating Exact- and Ranked Partially-Matched Answers to Questions in Advertisements
Summary: CQAds converts natural-language ad questions to SQL without full syntactic parsing, inferring domains, Boolean structure, and incomplete criteria. It uniquely returns exact answers or domain-aware, similarity-ranked partial matches via best-guess processing. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Rani Qumsiyeh (Brigham Young University)
- 2. Maria S. Pera (Brigham Young University)
- 3. Yiu-Kai Ng (Brigham Young University)
BibTeX Citation
@article{qumsiyeh_vldb12,
title = {{Generating Exact- and Ranked Partially-Matched Answers to Questions in Advertisements}},
author = {Qumsiyeh, Rani and Pera, Maria S. and Ng, Yiu-Kai},
journal = {PVLDB},
series = {{VLDB} '12},
volume = {5},
number = {3},
pages = {217--228},
doi = {10.14778/2078331.2078337},
url = {https://doi.org/10.14778/2078331.2078337},
year = {2012}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 656 | Foundations of Preferences in Database Systems | 2002 | VLDB | 0.00015258172 |
| 1,526 | Ordering the Attributes of Query Results | 2006 | SIGMOD | 0.00010491673 |
| 5,961 | Answering Imprecise Queries over Web Databases | 2005 | VLDB | 6.0274692e-05 |
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