Structured Subject Category Taxonomy And Hierarchical Organization

57-subject knowledge benchmark — 15K+ questions across STEM, humanities, professional domains.

Unique: Encodes a structured taxonomy of 57 subjects into 4 categories as a centralized, reusable data structure (categories.py), enabling consistent categorization across all evaluation and analysis code. This separation of taxonomy definition from evaluation logic allows researchers to analyze results at multiple levels of granularity without duplicating category mappings.

vs others: Provides a centralized, version-controlled taxonomy compared to ad-hoc category definitions scattered across analysis scripts, ensuring consistency and enabling reproducible category-level analysis across publications.

via “academic subject taxonomy and hierarchical filtering”

Dataset by cais. 4,76,392 downloads.

Unique: Explicit subject labels for every question enable filtering without external knowledge graphs or NLP-based categorization. 57-subject taxonomy is comprehensive and expert-validated, covering STEM, humanities, social sciences, and professional domains in single dataset.

vs others: More granular than generic QA datasets (SQuAD, RACE) while maintaining simplicity of flat taxonomy versus complex hierarchical ontologies

via “concept-hierarchy-organization”