Do General NLP Embeddings Capture Ontological Reasoning?

2026-08-31

AI Summary

研究探讨了通用NLP嵌入模型是否能捕捉本体论逻辑结构，通过AVA框架评估171,007个对比三元组，发现即使是最先进的模型也存在显著局限。

Abstract

General-purpose NLP embedding models perform well on linguistic tasks, but their ability to capture symbolic ontological structure remains unclear. We introduce AVA, a systematic framework for evaluating whether embeddings distinguish logic-sensitive relational semantics in ontologies and knowledge graphs. AVA comprises 171,007 contrastive triplets derived from 163 heterogeneous ontologies using hierarchy inversion, relation substitution, and disjointness injection. Each triplet contains an ontology statement, a semantically equivalent paraphrase, and a logic-sensitive hard negative with contradictory relational meaning. We evaluate more than 25 state-of-the-art embedding models and find substantial limitations: the best model achieves only 0.739 triplet accuracy, while hard negative accuracy falls to 0.135. Fine-tuning improves discrimination by a large margin but transfers poorly to downstream Semantic Web tasks, including taxonomy discovery and ontology alignment. Further analysis suggests that improvements stem partly from perturbation-specific pattern recognition rather than robust ontological understanding. These findings reveal a persistent gap between linguistic representation learning and ontology-level discrimination, challenging the assumption that strong NLP benchmark performance translates to Semantic Web competence.

Problem

• NLP embeddings
• ontological reasoning
• relational semantics

Innovation

• AVA framework
• ontological reasoning
• embedding models
• contrastive triplets
• Semantic Web tasks