# 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.