@article{watson-etal-2026-frame2kg,
    title = "{F}rame2{KG}: A Benchmark and Evaluation Toolkit for Interpretable Frame-to-Graph Generation",
    author = "Watson, Lewis N.  and
      Strathearn, Carl  and
      Mitchell, Kenny  and
      Yu, Yanchao",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.854/",
    pages = "10912--10926",
    abstract = "Interpretable frame-to-knowledge-graph (Frame2KG) generation enables structured visual scene representation while supporting on-device inference to enhance privacy, improve interpretability, and minimise compute. We introduce Frame2KG-YC2, a synthetic, reproducible dataset derived from YouCook2 that pairs keyframes with schema-valid JSON knowledge graphs containing typed, spatially grounded entities and semantic predicates, alongside faithful textual paraphrases. Using this corpus, we fine-tune Qwen2.5-VL models (3B and 7B) with parameter-efficient LoRA adapters on attention layers (QKVO), with and without GateProj/Up/Down MLP projections. For evaluation and benchmarking, we propose a deterministic toolkit featuring two-stage node matching, an IoU gate followed by Hungarian assignment on blended spatial-semantic similarity, and comprehensive metrics spanning node/edge precision-recall-F1, matched-pair IoU, and structural validity. On a held-out test set, our models achieve Node F1{\ensuremath{\mu}} up to 0.621 and Edge F1{\ensuremath{\mu}} up to 0.208, with mean matched IoU of {\ensuremath{\approx}}0.61 and {\ensuremath{>}}98{\%} schema conformity. We show that MLP gating consistently improves predicate accuracy and spatial grounding, while post-training quantisation maintains accuracy and improves deployability on edge hardware. We release the dataset, code, adapters, and evaluation toolkit to establish an open, interpretable baseline for future temporal and multi-view extensions."
}