Retrieve-and-Verify: A Table Context Selection Framework for Accurate Column Annotations

Summary: Introduce a retrieve-and-verify framework that selects compact, informative column contexts (instead of serializing whole tables) via unsupervised retrieval balancing relevance and diversity and role-aware target-context encoding (REVEAL). REVEAL+ adds a learned verification classifier with top-down inference to efficiently refine context subsets (quadratic search), yielding consistent CTA/CPA accuracy gains over SOTA across six benchmarks. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID: h5a0111ff0eb837ff
Venue: SIGMOD
Year: 2026
Pagerank: 5.1064323e-05
Overall Rank: 9,838 | 34.06%
DOI: 10.1145/3769823
PDF: Download (CC BY 4.0)

Incoming Non-self Citations Over Time

Authors

1. Zhihao Ding (Hong Kong Polytechnic University)
2. Yongkang Sun (Hong Kong Polytechnic University)
3. Jieming Shi (Hong Kong Polytechnic University)

BibTeX Citation

@inproceedings{ding_sigmod26,
        title = {{Retrieve-and-Verify: A Table Context Selection Framework for Accurate Column Annotations}},
        author = {Ding, Zhihao and Sun, Yongkang and Shi, Jieming},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3769823},
        url = {https://dl.acm.org/doi/10.1145/3769823},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,409	SemDistill: Bootstrapping a Low-Latency, Low-Cost Semantic Table Annotator from Noisy LLM	2027	SIGMOD	4.9605343e-05

Outgoing Citations (Sorted by Pagerank)

Showing 19 of 19 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
366	TURL: Table Understanding through Representation Learning	2021	VLDB	0.00019902067
679	JOSIE: Overlap Set Similarity Search for Finding Joinable Tables in Data Lakes	2019	SIGMOD	0.00014802
758	Table Union Search on Open Data	2018	VLDB	0.00014200657
1,669	Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks	2024	SIGMOD	9.9157758e-05
1,690	Annotating Columns with Pre-trained Language Models	2022	SIGMOD	9.8426663e-05
1,742	Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning	2023	VLDB	9.7283207e-05
1,929	SANTOS: Relationship-based Semantic Table Union Search	2023	SIGMOD	9.3458857e-05
1,985	Sato: Contextual Semantic Type Detection in Tables	2020	VLDB	9.2378767e-05
2,334	GitTables: A Large-Scale Corpus of Relational Tables	2023	SIGMOD	8.6058607e-05
2,370	DeepJoin: Joinable Table Discovery with Pre-trained Language Models	2023	VLDB	8.5469172e-05
3,166	Unicorn: A Unified Multi-tasking Model for Supporting Matching Tasks in Data Integration	2023	SIGMOD	7.5597853e-05
4,158	Integrating Data Lake Tables	2023	VLDB	6.7595989e-05
4,335	ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models	2024	VLDB	6.6432811e-05
4,419	LakeBench: A Benchmark for Discovering Joinable and Unionable Tables in Data Lakes	2024	VLDB	6.5905711e-05
4,964	PrivLava: Synthesizing Relational Data with Foreign Keys under Differential Privacy	2023	SIGMOD	6.3234288e-05
5,120	Explaining Dataset Changes for Semantic Data Versioning with Explain-Da-V	2023	VLDB	6.254003e-05
7,787	RECA: Related Tables Enhanced Column Semantic Type Annotation Framework	2023	VLDB	5.4356666e-05
8,161	Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation	2023	SIGMOD	5.37207e-05
9,073	Generation of Training Examples for Tabular Natural Language Inference	2023	SIGMOD	5.2117663e-05

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