Reference-Free Evaluation of Taxonomies
Abstract
We introduce two reference-free metrics for quality evaluation of taxonomies in the absence of labels.The first metric evaluates robustness by calculating the correlation between semantic and taxonomic similarity, addressing error types not considered by existing metrics.The second uses Natural Language Inference to assess logical adequacy.Both metrics are tested on five taxonomies and are shown to correlate well with F1 against ground truth taxonomies.We further demonstrate that our metrics can predict downstream performance in hierarchical classification when used with label hierarchies.
Fields of Study & Subjects
Biomedical Text Mining and Ontologies
Topic Modeling
Data Quality and Management
Publication details
- Journal / source
- Findings of the Association for Computational Linguistics: ACL 2026
- Publisher
- Association for Computational Linguistics
- Pages
- 25489–25507
- Publication date
- 1 January 2026
- Language
- EN