tools-only Adding Taxonomy to CSV Workflow
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This interactive Mermaid diagram shows the complete workflow for adding taxonomy categorization to a learning graph CSV file.
Interactive Diagram
Process Overview
This workflow demonstrates how to add taxonomy categorization to an existing learning graph CSV file. The process supports both automated (script-based) and manual categorization approaches.
Key Steps
- Identify Natural Categories - Review concept labels and group by topic, domain, or complexity level
- Design Taxonomy Abbreviations - Create 3-5 letter codes (FOUND, BASIC, ARCH, etc.)
- Choose Categorization Method - Select between automated (add-taxonomy.py) or manual assignment
- Review Assignments - Check that categorization makes logical sense
- Validate Distribution - Run taxonomy-distribution.py to ensure balance
- Adjust if Needed - Refine categories until distribution is balanced (no category > 30%)
Decision Points
Automated vs Manual: The add-taxonomy.py script uses keyword matching for initial suggestions, best for large graphs (150+ concepts). Manual assignment gives more control, recommended for smaller graphs or specialized domains.
Distribution Check: A balanced distribution ensures no single taxonomy dominates. Target: no category exceeding 30% of total concepts.