Making Table Understanding Work in Practice

Summary: Identifies a gap between high-accuracy DL table-understanding models on benchmarks and real-world deployments that still rely on heuristics/regex for a few semantic types. Characterizes practical needs—robustness, coverage, explainability, efficiency, calibration, domain adaptation—and prescribes evaluation and design directions to make table understanding usable in production. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: ha1c5f1cd5580fdcf
Venue: CIDR
Year: 2022
Pagerank: 5.2056825e-05
Overall Rank: 9,244 | 37.85%
DOI: -

Incoming Non-self Citations Over Time

Authors

1. Madelon Hulsebos (Sigma Computing; University of Amsterdam)
2. Sneha Gathani (Sigma Computing; University of Maryland)
3. James Gale (Sigma Computing)
4. Isil Dillig (University of Texas)
5. Paul Groth (University of Amsterdam)
6. Çağatay Demiralp (Sigma Computing)

BibTeX Citation

@inproceedings{hulsebos_cidr22,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '22},
        title = {{Making Table Understanding Work in Practice}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Hulsebos, Madelon and Gathani, Sneha and Gale, James and Dillig, Isil and Groth, Paul and Demiralp, Çağatay},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,720	Steered Training Data Generation for Learned Semantic Type Detection	2023	SIGMOD	4.9793485e-05

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
377	TURL: Table Understanding through Representation Learning	2021	VLDB	0.00019570264
2,521	GitTables: A Large-Scale Corpus of Relational Tables	2023	SIGMOD	8.3479333e-05

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