Medication errors pose a significant threat to patient safety, making pharmacist verification (PV) a critical, yet heavily burdened, final safeguard. The direct application of Large Language Models (LLMs) to this zero-tolerance domain is untenable due to their inherent factual unreliability, lack of traceability, and weakness in complex reasoning. To address these challenges, we introduce PharmGraph-Auditor, a novel system designed for safe and evidence-grounded prescription auditing. The core of our system is a trustworthy Hybrid Pharmaceutical Knowledge Base (HPKB), implemented under the Virtual Knowledge Graph (VKG) paradigm. This architecture strategically unifies a relational component for set constraint satisfaction and a graph component for topological reasoning via a rigorous mapping layer. To construct this HPKB, we propose the Iterative Schema Refinement (ISR) algorithm, a framework that enables the co-evolution of both graph and relational schemas from medical texts. For auditing, we introduce the KB-grounded Chain of Verification (CoV), a new reasoning paradigm that transforms the LLM from an unreliable generator into a transparent reasoning engine. CoV decomposes the audit task into a sequence of verifiable queries against the HPKB, generating hybrid query plans to retrieve evidence from the most appropriate data store. Experimental results demonstrate robust knowledge extraction capabilities and show promises of using PharmGraph-Auditor to enable pharmacists to achieve safer and faster prescription verification.

翻译：用药错误对患者安全构成重大威胁，使得药师审核成为关键但负担沉重的最后防线。由于大型语言模型存在固有的事实不可靠性、缺乏可追溯性以及在复杂推理方面的不足，将其直接应用于这一零容忍领域是不可行的。为解决这些挑战，我们提出了PharmGraph-Auditor——一个专为安全且基于证据的处方审核设计的新颖系统。该系统的核心是建立在虚拟知识图谱范式下的可信混合药学知识库。该架构通过严格的映射层，策略性地整合了用于集合约束满足的关系型组件与用于拓扑推理的图结构组件。为构建此混合药学知识库，我们提出了迭代模式优化算法，该框架支持从医学文本中协同演化图模式与关系模式。针对审核任务，我们提出了知识库驱动的验证链——一种将大型语言模型从不可靠的生成器转变为透明推理引擎的新型推理范式。验证链将审核任务分解为一系列针对混合药学知识库的可验证查询，通过生成混合查询计划从最合适的数据存储中检索证据。实验结果表明该系统具备强大的知识抽取能力，并展现了利用PharmGraph-Auditor帮助药师实现更安全、更快速处方验证的应用前景。