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Example-Driven Query Intent Discovery: Abductive Reasoning using Semantic Similarity
Summary: SQUID extends QBE to semantic similarity, solving intent via abductive reasoning over open-world data to auto-create SPJ queries. Precomputed semantic properties in abduction-ready DBs enable inference, yielding accurate intents and outperforming baselines.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
hf16eaf955bf76bcf
Venue
VLDB
Year
2019
Pagerank
7.2516839e-05
Overall Rank
3,504 | 76.45%
DOI
10.14778/3342263.3342266
Incoming Non-self Citations Over Time
BibTeX Citation
Copy BibTeX
@article{fariha_vldb19,
title = {{Example-Driven Query Intent Discovery: Abductive Reasoning using Semantic Similarity}},
author = {Fariha, Anna and Meliou, Alexandra},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {11},
pages = {1262--1275},
doi = {10.14778/3342263.3342266},
url = {https://doi.org/10.14778/3342263.3342266},
year = {2019}
}
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Pagerank
38
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2002
VLDB
0.00047394041
861
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2011
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1,211
Query From Examples: An Iterative, Data-Driven Approach to Query Construction
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2014
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Sample-Driven Schema Mapping
2012
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FastQRE: Fast Query Reverse Engineering
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SPARQLByE: Querying RDF data by example
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6.7285738e-05
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SnapToQuery: Providing Interactive Feedback during Exploratory Query Specification
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SQuID: Semantic Similarity-Aware Query Intent Discovery
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SIGMOD
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