Researchers Develop Reference-Free Metrics for Evaluating Taxonomy Quality
Researchers have introduced two new metrics that can evaluate the quality of taxonomies without requiring ground-truth reference labels. The first metric measures robustness by correlating semantic and taxonomic similarity, while the second uses Natural Language Inference to assess logical adequacy. The work addresses a gap in taxonomy evaluation and shows promise for improving hierarchical classification systems.
A preprint posted to arXiv presents two reference-free metrics designed to assess taxonomy quality in settings where labeled ground truth is unavailable. The first metric evaluates robustness by computing the correlation between semantic similarity and taxonomic similarity, targeting error types overlooked by existing approaches. The second metric leverages Natural Language Inference (NLI) to determine whether a taxonomy's hierarchical relationships are logically adequate. Both metrics were tested across five taxonomies and demonstrated strong correlation with F1 scores computed against ground-truth taxonomies, suggesting they are reliable proxies for standard evaluation. Additionally, the authors show that their metrics can predict downstream performance in hierarchical classification tasks when applied to label hierarchies, indicating practical utility beyond taxonomy construction alone. The paper has undergone multiple revisions since its initial submission in May 2025, with the most recent version posted in June 2026.
What's missing
It is unclear how computationally expensive the NLI-based metric is at scale, or how the metrics perform when taxonomies are very large or highly specialized. The relationship between the two metrics—whether they are complementary or redundant in practice—is not fully characterized in the abstract.
What different sources said
- arXiv cs.CLCenter
Reference-Free Evaluation of Taxonomies
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