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AVA is introduced, a systematic framework for evaluating whether embeddings distinguish logic-sensitive relational semantics in ontologies and knowledge graphs, and reveals a persistent gap between linguistic representation learning and ontology-level discrimination, challenging the assumption that strong NLP benchmark...
It is revealed that ensemble composition directly affects the precision-recall trade-off: heterogeneous cross-paradigm ensembles generally improve precision, whereas homogeneous LLM ensembles more often achieve higher overall F1-scores.
Findings show that model size alone is an insufficient selection criterion for OL and provide empirical guidance for reproducible LLM-assisted ontology engineering and indicate that architecture and model lineage can outweigh nominal parameter count.
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