---
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

<p align="left">
  <img src="assets/cta.png" alt="CTA Example" width="800">
</p>

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:

```bibtex
@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. |