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Frame2KG Documentation Hub

Frame2KG aggregates the data, models, and tooling introduced in Frame2KG-YC2: A Synthetic Dataset, LoRA Adapters, and an Evaluation Toolkit for Frame → Graph. The goal is to provide a single launch point for reproducible frame-to-knowledge-graph research focused on interpretable, on-device robotics.

Frame2KG Example

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Update Log

2026-04-13:

  • Improved evaluation efficiency (shared embedding model, reduced per-frame overhead)
  • Standardised evaluation defaults for node text fields (label, attributes)

Note on evaluation:
The evaluation toolkit uses label, attributes as the default node text fields.
For exact reproduction of the LREC 2026 paper results, please refer to the evaluation repository documentation.

Citing

@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}
  }

Contact

For questions or collaboration inquiries, reach out to l.watson{at}napier.ac.uk.

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A Benchmark and Evaluation Toolkit for Interpretable Frame-to-Graph Generation

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