Abstract
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.
Abstract (translated)
知识图谱（KGs）是人工智能系统的重要基础，但其自动化构建过程不可避免地引入噪声，影响数据可信度。现有的基于图嵌入或语言模型的三元组验证方法，往往因过度依赖内部结构约束或外部语义证据而存在单源偏差，且通常遵循静态推理范式。因此，这些方法难以处理复杂或长尾事实，且可解释性有限。针对上述局限，本文提出SHARP（模式感知混合智能可靠预测系统），一种无需训练的自主智能体，将三元组验证重构为动态的战略规划、主动调查与证据推理过程。具体而言，SHARP通过记忆增强机制与模式感知战略规划提升推理稳定性，并采用增强型ReAct循环与混合知识工具集，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显著超越现有最优基线，准确率分别提升4.2%和12.9%。此外，SHARP为每个判断提供透明的、基于事实的证据链，在复杂验证任务中展现出强大的可解释性与鲁棒性。
URL
https://arxiv.org/abs/2604.04190
