Schema-Aware Planning and Hybrid Knowledge Toolset for Reliable Knowledge Graph Triple Verification

Abstract:Knowledge Graphs (KGs) provide a critical foundation for many AI systems, yet their automated construction inevitably introduces noisy triples that undermine data trustworthiness. Reliable KG auditing requires jointly examining internal structural constraints and open-world evidence, whereas existing methods often rely on a single evidence source or static, one-shot inference, limiting adaptive evidence acquisition and auditability. We propose SHARP (Schema-Hybrid Agent for Reliable Prediction), a training-free agent that reformulates triple verification as a dynamic process of strategic planning, active investigation, and evidential reasoning. SHARP uses retrieved historical verification trajectories and entity--relation schema information to construct a task-specific investigation plan. It then iteratively coordinates KG and textual tools to cross-validate structural and semantic evidence, producing a verification verdict with a transparent evidence chain. On independently sampled, balanced test sets constructed from FB15K-237 and Wikidata5M-Ind, SHARP achieves average accuracies of 87.1% and 93.6%, respectively. Controlled comparisons with standard RAG using the same evidence sources, together with ablation studies, further show that the gains arise not merely from external retrieval, but from the combined effects of schema-aware planning, memory augmentation, and iterative hybrid tool use.

Artificial Intelligence (cs.AI)

arXiv:2604.04190 [cs.AI]

(or arXiv:2604.04190v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2604.04190 Focus to learn more arXiv-issued DOI via DataCite

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