---
dataset_info:
  features:
  - name: video_id
    dtype: string
  - name: frame_number
    dtype: string
  - name: category
    dtype: string
  - name: image
    dtype: image
  - name: graph
    struct:
    - name: edges
      list:
      - name: predicate
        dtype: string
      - name: source
        dtype: string
      - name: target
        dtype: string
    - name: nodes
      list:
      - name: attributes
        struct:
        - name: appearance
          dtype: string
        - name: size
          dtype: string
      - name: id
        dtype: string
      - name: label
        dtype: string
      - name: location
        dtype: string
  - name: graph_sentence
    dtype: string
  splits:
  - name: training
    num_bytes: 486523923
    num_examples: 12512
  - name: train_calibration
    num_bytes: 9893243
    num_examples: 256
  - name: validation
    num_bytes: 117826126
    num_examples: 3357
  - name: testing
    num_bytes: 112245657
    num_examples: 2849
  - name: validation_dev
    num_bytes: 3297153
    num_examples: 100
  download_size: 710966092
  dataset_size: 729786102
configs:
- config_name: default
  data_files:
  - split: training
    path: data/training-*
  - split: train_calibration
    path: data/train_calibration-*
  - split: validation
    path: data/validation-*
  - split: testing
    path: data/testing-*
  - split: validation_dev
    path: data/validation_dev-*
---

# Frame2KG-YC2

Frame2KG-YC2 is a frame-to-knowledge-graph synthetic dataset derived from YouCook2, a cooking video dataset.

Each example contains an image, source metadata, a structured graph of localised entities and relations, and a short graph-derived sentence.

## Splits

The `training`, `validation`, and `testing` splits are the primary dataset splits.

The `validation_dev` split is a 100-example subsample of `validation`.

The `train_calibration` split is a 256-example subsample of `training`, intended for calibration or quantisation workflows.

## Citation

If you use this dataset in your work, please cite the paper:

```bibtex
@inproceedings{watson2026frame2kg,
  title = {Frame2KG: A Benchmark and Evaluation Toolkit for Interpretable Frame-to-Graph Generation},
  author = {Watson, Lewis N. and Strathearn, Carl and Mitchell, Kenny and Yu, Yanchao},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026)},
  month = {May},
  year = {2026},
  pages = {10912--10926},
  address = {Palma, Mallorca, Spain},
  publisher = {European Language Resources Association (ELRA)},
  editor = {Piperidis, Stelios and Bel, Núria and van den Heuvel, Henk and Ide, Nancy and Krek, Simon and Toral, Antonio},
  doi = {10.63317/4ys6kofrzoc5},
  url = {https://doi.org/10.63317/4ys6kofrzoc5}
```

## Notes

Annotations are synthetic and cooking-domain specific, so the dataset should be treated as a controlled benchmark rather than general-purpose visual ground truth. Dataset is provided as is.