| license: apache-2.0 | |
| # DiagramQG: A Dataset for Generating Concept-Focused Questions from Diagrams | |
| # DiagramQG Dataset | |
|  | |
| *Figure 1: Four different examples of different subjects in DiagramQG dataset.* | |
|  | |
| *Figure 2: Domain diversity in DiagramQG. Each color corresponds to one subject: Natural Science (blue), Earth Science (yellow), Applied Science (green), and Social Science (orange).* | |
| ## Overview | |
| DiagramQG is a comprehensive educational dataset focused on scientific diagram question generation. It contains: | |
| - 19,475 unique questions | |
| - 8,372 diagrams | |
| - 44,472 combinations of (target & concept text constraint, diagram, question) | |
| - Coverage across 4 subjects, 15 courses, and 169 concepts | |
| # Due to the ongoing peer review process of our research paper, we are currently releasing a subset of the DiagramQG dataset. # | |
| ## Dataset Structure | |
| ### Subject Areas | |
| The dataset covers four main subject areas: | |
| - Natural Science | |
| - Earth Science | |
| - Applied Science | |
| - Social Science | |
| ### Hierarchical Organization | |
| Data is organized hierarchically: | |
| 1. Subject (e.g., Natural Science) | |
| 2. Course (e.g., Biology) | |
| 3. Concept (e.g., Ecological interactions) | |
| ## Data Collection Process | |
| ### Phase 1: Initial Data Gathering | |
| - Sources: Existing datasets and Google Image Search | |
| - Raw dataset: 20,000+ diagrams and 40,000+ questions | |
| ### Phase 2: Organization | |
| - Classification into 4 subjects and 15 courses | |
| - Mapping questions to 169 distinct concepts | |
| ### Phase 3: Annotation | |
| - Trained crowd workers annotate: | |
| - Target & concept text constraints | |
| - Diagram elements and texts | |
| - Produced 70,000+ unique combinations | |
| ### Phase 4: Quality Assurance | |
| - Secondary crowd worker evaluation (0-100 scale) | |
| - Filtered combinations below 60 points | |
| - Final dataset: 44,472 validated combinations | |
| ## Dataset Analysis | |
| ### Question Distribution | |
|  | |
| *Figure 3: Question distribution in DiagramQG.* | |
| ### Concept Distribution | |
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| *Figure 4: Distribution of diagrams, questions, and questions per diagram ratios across different concepts in DiagramQG.* | |
| ### Dataset Comparison | |
| | Dataset | Questions | Images | Objects/Image | Image Type | Constraints | Knowledge Type | | |
| |---------|-----------|---------|---------------|------------|-------------|----------------| | |
| | VQAv2.0 | 1.1M | 20k | 3.5 | natural | answer | N/A | | |
| | FVQA | 5,826 | 2k | 2.9 | natural | answer | common-sense | | |
| | VQG-COCO | 25,000 | 5k | 3.3 | natural | image, caption | common-sense | | |
| | K-VQG | 16,098 | 13K | 2.7 | natural | knowledge triple | common-sense | | |
| | DiagramQG | 19,475 | 8,372 | 11.2 | diagram | target, concept | subject knowledge | | |
| ## Unique Challenges | |
| 1. **Domain-specific Knowledge Requirement** | |
| - Requires understanding of specialized subject concepts | |
| - Goes beyond common sense reasoning | |
| 2. **Long-tail Distribution** | |
| - Uneven concept coverage | |
| - Challenges in model generalization | |
| 3. **High Information Density** | |
| - Complex diagram interpretation | |
| - Dense visual information processing |