arXiv: cs.AI preprintsInternational6 November 2026
Bridging Structure and Semantics: Knowledge Graph Verification via Triple-to-Text Alignment
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arXiv:2611.07780v1 Announce Type: cross Abstract: Semantic accuracy is an important dimension of knowledge graph quality. Graph-based scoring methods exploit relational structure, while textual representations allow classifiers to draw on knowledge encoded by pre-trained language models (PLMs). We present GenVer, a generative verifier that scores triples using both their serialized form and generated natural language representations (NLRs). A triple-text alignment procedure selects training pairs for a sequence-to-sequence generator; a BERT-based classifier then combines features from each tri
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