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

论文概要

• 领域: ML
• 作者: Xinyan Ma, Xianhao Ou, Weihao Zhang

中文摘要

本文提出了 SHARP (Schema-Hybrid Agent for Reliable Prediction)，一个无需训练、具备自主智能的知识图谱三元组验证框架。SHARP 将三元组验证重构为动态的战略规划、主动调查和证据推理过程。该系统结合了记忆增强机制与模式感知战略规划来提升推理稳定性，并通过增强的 ReAct 循环和混合知识工具集动态整合知识图谱内部结构与外部文本证据进行交叉验证。在 FB15K-237 和 Wikidata5M-Ind 数据集上的实验表明，SHARP 分别实现了 4.2% 和 12.9% 的准确率提升。

原文摘要

Knowledge Graphs (KGs) serve as a critical foundation for AI systems, yet their automated construction inevitably introduces noise, compromising data trustworthiness. Existing triple verification methods, based on graph embeddings or language models, often suffer from single-source bias by relying on either internal structural constraints or external semantic evidence, and usually follow a static inference paradigm. As a result, they struggle with complex or long-tail facts and provide limited interpretability. To address these limitations, we propose SHARP (Schema-Hybrid Agent for Reliable Prediction), a training-free autonomous agent that reformulates triple verification as a dynamic process of strategic planning, active investigation, and evidential reasoning. Specifically, SHARP combines a Memory-Augmented Mechanism with Schema-Aware Strategic Planning to improve reasoning stability, and employs an enhanced ReAct loop with a Hybrid Knowledge Toolset to dynamically integrate internal KG structure and external textual evidence for cross-verification. Experiments on FB15K-237 and Wikidata5M-Ind show that SHARP significantly outperforms existing state-of-the-art baselines, achieving accuracy gains of 4.2% and 12.9%, respectively. Moreover, SHARP provides transparent, fact-based evidence chains for each judgment, demonstrating strong interpretability and robustness for complex verification tasks.

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