Overview
The OpenReview Processing pipeline provides a complete workflow for transforming raw OpenReview data into structured knowledge graphs...
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End-to-end pipeline for transforming OpenReview data into graph format.
The OpenReview Processing pipeline provides a complete workflow for transforming raw OpenReview data into structured knowledge graphs...
| Stage | Description | Components | Output |
|---|---|---|---|
1. Data Collection |
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2. PDF Processing |
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3. Entity Extraction |
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4. Relation Construction |
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5. Graph Assembly |
| Entity Type | Source | Key Attributes |
|---|---|---|
Submission |
OpenReview API | |
Review |
OpenReview API | |
Decision |
OpenReview API | |
Rebuttal |
OpenReview API | |
Venue |
OpenReview API |
| Relation Type | Source → Target | Description |
|---|---|---|
SUBMITTED_TO |
Paper → Venue | |
REVIEWS |
Review → Paper | |
DECIDES |
Decision → Paper | |
REBUTS |
Rebuttal → Review | |
REVISES |
Paper → Paper |
# Add requirements here
pip install openreview-py
pip install networkx
# etc.
# Add configuration instructions
# Example configuration file or setup code
# Add code example for running complete pipeline
# Example: Process all ICLR 2024 submissions
# Add code example for processing a single venue
# Example: Process NeurIPS 2024
# Add code example for incremental processing
# Example: Update existing data with new submissions
# Add code example for custom entity extraction
# Example: Extract custom metadata fields
# Add code example for exporting to storage
# Example: Save to SQL, CSV, or JSON
The processing pipeline generates a graph structure following our entity and relation specifications.
| Parameter | Type | Default | Description |
|---|---|---|---|
|
Avg. Processing Time
Submissions Processed
Success Rate
Avg. Entities per Paper
Solution:
Solution: