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

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