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Element Graph v2.1 — Multimodal Academic Document Retrieval

Typed element graphs derived from three multimodal academic document QA datasets: SPIQA, SciEGQA, and MMDocIR. Each document is decomposed into a typed graph with cross-modal edges (caption_of), reference edges (refer_to), and section-aware structural edges (contains, reading_next, section_next).

The graphs are designed for retrieval research:

  • Training supervision via Graph-Relevance Contrastive Loss (GRCL)
  • Inference-time graph propagation of retrieval scores
  • Same edge-weight schema drives both (single source of truth)

Contents

Subset Papers Elements Caption-figure pairs Refer_to edges
spiqa-v2.1/train 25,459 1,873,124 262,524 231,101
spiqa-v2.1/val 200 14,537 2,085 1,947
spiqa-v2.1/test-A 118 9,039 1,115 1,191
sciegqa-v2.1 80 21,817 444 1,103
mmdocir-v2.1 75 15,434 203 475

Per-document section count

Dataset min median mean max
spiqa-v2.1 (test-A; LaTeX-based) 1 7 7.2 24
sciegqa-v2.1 (docling-based) 1 10 12.8 54
mmdocir-v2.1 (docling-based) 3 9 13.1 122

Schema

See SCHEMA.md for full schema definition (node types, edge types, attributes, target-attribute modifiers).

Provenance

  • SPIQA: graph extracted from original LaTeX (\section{}, \ref{}) and all_figures metadata. Caption-figure pairs are author-provided.
  • SciEGQA: graph derived from docling layout parsing + heuristic extraction.
  • MMDocIR: graph derived from MMDocIR_layouts.parquet + heuristic extraction.

Reference (original datasets)

  • SPIQA: Pramanick et al., NeurIPS 2024 D&B
  • SciEGQA: arXiv:2511.15090
  • MMDocIR: Dong et al., EMNLP 2025

Images

Image files are NOT included. Download from original dataset sources and look up by (doc_id, eid) — the eid is preserved as the figure filename (SPIQA) or layout_id (MMDocIR).

License

Each subset inherits the license of the original dataset. See per-subset README files.

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