README.md

metadata

pretty_name: Table Column Annotation Benchmarks (CTA/CPA) for REVEAL
tags:
  - table-understanding
  - column-annotation
  - semantic-typing
  - relation-extraction
  - column-type-annotation
  - column-property-annotation
  - data-management
  - CTA
  - CPA
task_categories:
  - text-classification
  - token-classification
  - other
license: cc-by-4.0
annotations_creators:
  - machine-generated
  - expert-generated

Table Column Annotation Benchmarks (CTA/CPA) for REVEAL

This dataset repository accompanies the paper:

Retrieve-and-Verify: A Table Context Selection Framework for Accurate Column Annotations
Zhihao Ding, Yongkang Sun, Jieming Shi. Proc. ACM Manag. Data (Dec 2025).

The work targets two column annotation tasks:

• Column Type Annotation (CTA): assign a semantic type to a target column.
• Column Property Annotation (CPA) (a.k.a. column relation/property annotation): assign a semantic relation/property between a target column and another column.

Citation

If you use this dataset, please cite:

@article{10.1145/3769823,
  author = {Ding, Zhihao and Sun, Yongkang and Shi, Jieming},
  title = {Retrieve-and-Verify: A Table Context Selection Framework for Accurate Column Annotations},
  year = {2025},
  issue_date = {December 2025},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {3},
  number = {6},
  url = {https://doi.org/10.1145/3769823},
  doi = {10.1145/3769823},
  journal = {Proc. ACM Manag. Data},
  month = dec,
  articleno = {358},
  numpages = {27},
  keywords = {column annotation, context selection, embeddings, table understanding}
}

Dataset summary

This repository provides dataset artifacts for running and reproducing experiments in the paper above.

Benchmarks used in the paper

Benchmark	# Tables	# Types	Total # Cols	# Labeled Cols	Min/Max/Avg Cols per Table
GitTablesDB	3,737	101	45,304	5,433	1 / 193 / 12.1
GitTablesSC	2,853	53	34,148	3,863	1 / 150 / 12.0
SOTAB-CTA	24,275	91	195,543	64,884	3 / 30 / 8.1
SOTAB-CPA	20,686	176	196,831	74,216	3 / 31 / 9.5
WikiTable-CTA	406,706	255	2,393,027	654,670	1 / 99 / 5.9
WikiTable-CPA	55,970	121	306,265	62,954	2 / 38 / 5.5

What is included in this Hugging Face dataset repository?

• GitTablesDB (gt-semtab22-dbpedia-all): raw CSV tables with 5-fold splits.
• GitTablesSC (gt-semtab22-schema-property-all): raw CSV tables with 5-fold splits.
• SOTAB-CTA / SOTAB-CPA / WikiTables-CTA / WikiTables-CPA: official train/validation/test splits.

For each dataset, type_vocab.txt provides the mapping between type IDs and raw type names.

Dataset structure

Task name mapping (paper ↔ codebase)

Paper Name	Codebase Task Name
GitTablesDB	gt-semtab22-dbpedia-all
GitTablesSC	gt-semtab22-schema-property-all
SOTAB-CTA	sotab
SOTAB-CPA	sotab-re
WikiTables-CTA	turl
WikiTables-CPA	turl-re

Data schema (columns)

Column	Type	Description
table_id	string	Identifier of the source table.
column_id	int	0-based index of the target column within the table.
label	string	Ground-truth type id. For multi-class tasks, a single type ID (-1 indicates unlabeled). For multi-label tasks, a binary list encoded as a string (e.g., [1,0,0,...]).
data	string	Cell values of the target column in original order, serialized as a single string.