arXiv:2505.11470v3 Announce Type: replace 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 hierarchi
The proliferation of AI systems across various domains intensifies the need for robust and automated evaluation methods, especially for complex knowledge structures like taxonomies.
Improved, reference-free taxonomy evaluation directly enhances the development and reliability of AI systems that rely on structured knowledge, such as reasoning engines and agents.
The introduction of reference-free metrics for taxonomy evaluation provides new, scalable tools for assessing the quality of knowledge hierarchies without human-labeled ground truth.
- · AI developers
- · Knowledge graph builders
- · Natural Language Processing researchers
- · AI-driven semantic search companies
- · Companies relying solely on manual taxonomy validation
- · Legacy knowledge management systems for evaluation
Faster and more accurate development of robust AI knowledge bases.
Acceleration of self-improving AI systems capable of evaluating and refining their own knowledge structures.
Potentially enables more complex and reliable AI agents to emerge, given improved foundational knowledge representation.
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Read at arXiv cs.CL