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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{}) andall_figuresmetadata. 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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