---
title: "Frame2KG: A Benchmark and Evaluation Toolkit — Watson 2026"
description: "The paper targets a missing resource: there is no open, reproducible benchmark for turning a single image into a localised knowledge graph that is…"
authors: ["Lewis N. Watson", "Carl Strathearn", "Kenny Mitchell", "Yanchao Yu"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.854/"
doi: "10.63317/4ys6kofrzoc5"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/watson-2026-frame2kg-benchmark-evaluation-toolkit-interpretable/"
---
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# Frame2KG: A Benchmark and Evaluation Toolkit for Interpretable Frame-to-Graph Generation

**Authors:** [Lewis N. Watson](https://md.research.elamur.ai/free/en/authors/lewis-n-watson/), [Carl Strathearn](https://md.research.elamur.ai/free/en/authors/carl-strathearn/), [Kenny Mitchell](https://md.research.elamur.ai/free/en/authors/kenny-mitchell/), [Yanchao Yu](https://md.research.elamur.ai/free/en/authors/yanchao-yu/) · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.854/) · [PDF](https://aclanthology.org/2026.lrec-1.854.pdf) · [DOI 10.63317/4ys6kofrzoc5](https://doi.org/10.63317/4ys6kofrzoc5) · [OpenAlex](https://openalex.org/W7160206624)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
The paper targets a missing resource: there is no open, reproducible benchmark for turning a single image into a localised knowledge graph that is checked both for correctness and for efficiency. Such graphs are meant for robots and other devices that run on-device. The authors release Frame2KG-YC2, a synthetic corpus built from YouCook2 cooking videos. Each sampled keyframe is paired with a strict-JSON graph and a short graph_sentence paraphrase. In the graph, nodes are typed objects with normalised boxes and confidences, and edges are predicates. Labels come from a GPT vision–language teacher, and outputs failing schema checks are regenerated. As baselines, Qwen2.5-VL 3B and 7B are fine-tuned with LoRA in four adapter configurations. A deterministic scoring toolkit first filters node pairs by box overlap, then assigns them one-to-one with Hungarian matching on combined spatial and text similarity. The best runs reach Node F1 µ of 0.621 and Edge F1 µ of 0.208, with over 98% schema conformity. These results show that compact, quantised VLMs can produce machine-readable, interpretable scene graphs.

## Key points
- **Data pipeline:** frames are taken from YouCook2 (2,000 videos, 89 recipes) at 1 fps. Near-duplicates are removed with OpenCLIP ViT-B/32 cosine similarity (τ = 0.90). Batched JSONL annotation cut API cost by about half.
- **Adapters:** LoRA is applied to attention projections (QKVO), with or without GateProj/Up/Down MLP projections. This trains under 0.5% of model weights.
- **Effect of gating (3B):** Edge F1 µ rose from 0.187 to 0.208, and mean matched IoU from 0.600 to 0.609. The larger 7B backbone mainly helps entity naming.
- **Recommended deployment choice:** 3B/QKVO-Gate scores Node F1 µ 0.616, close to 0.621 for 7B. It is equal or better on edges and IoU and needs much less memory.
- **Quantisation and predicate matching:** INT4 post-training quantisation costs little (val_dev Node F1 0.664 → 0.658). Embedding-based predicate matching roughly doubled compute without improving accuracy, so normalised string equality is the default.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```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},
  year = {2026},
  doi = {10.63317/4ys6kofrzoc5},
  url = {https://doi.org/10.63317/4ys6kofrzoc5}
}
```

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