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SQuID: Semantic Similarity-Aware Query Intent Discovery

Summary: SQuID discovers user intent from a few example tuples via semantic similarity and data-driven associations beyond surface structure. An interactive demo shows the semantic context and lets users refine results through feedback, no schema or SQL required. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5571
Venue
SIGMOD
Year
2018
Pagerank
5.2778771e-05
Overall Rank
9,368 | 35.73%
DOI
10.1145/3183713.3193548

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{fariha_sigmod18,
        title = {{SQuID: Semantic Similarity-Aware Query Intent Discovery}},
        author = {Fariha, Anna and Sarwar, Sheikh Muhammad and Meliou, Alexandra},
        series = {{SIGMOD} '18},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3183713.3193548},
        url = {https://dl.acm.org/doi/10.1145/3183713.3193548},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
3,677 Example-Driven Query Intent Discovery: Abductive Reasoning using Semantic Similarity 2019 VLDB 7.2076465e-05
6,461 Explain3D: Explaining Disagreements in Disjoint Datasets 2019 VLDB 5.8718966e-05
10,082 Wred: Workload Reduction for Scalable Index Tuning 2024 SIGMOD 5.1587525e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

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