Laboratory workflows in pharmaceutical and biomedical research encode substantial tacit knowledge -- expert judgment about failure conditions, decision branching logic, and contextual dependencies -- that remains inaccessible to protocol documents, sensor streams, and existing biomedical ontologies. We present a repeatable structured expert elicitation methodology and federated Semantic Knowledge Graph (SKG) architecture for capturing and querying this knowledge, demonstrated through deployment at the Biochemical and Cellular Pharmacology Department of Genentech. Knowledge is elicited via the Protocol Intelligence Co-pilot, a purpose-built AI interview agent that applies structured elicitation lenses to surface tacit procedural knowledge with expert-assigned confidence scores, producing graph representations across three tiers: program-level decision milestones, assay protocol knowledge, and physical execution infrastructure. Separately constructed subgraphs, exemplified by immunoassay (ELISA), quantitative mass spectrometry (LC-MS/PRM), and laboratory automation, are aligned through a shared upper ontology and queried as a single federated graph. Evaluation demonstrates seven query types structurally unavailable from any individual data source, including a cross-subgraph traversal that identifies automation-masked silent failures -- conditions where execution logs report success while scientific validity is compromised. Critically, the MASKED_BY graph relationship encodes a class of laboratory risk invisible to current informatics platforms -- the structural gap that prevents existing systems from reasoning about scientific validity. This architecture provides the semantic world model that AI laboratory agents currently lack: a queryable representation of where workflows fail silently, where human judgment is irreplaceable, and which execution assets mask rather than detect failure.

翻译：制药与生物医学研究中的实验室工作流程包含了大量隐性知识——关于失败条件、决策分支逻辑及上下文依赖性的专家判断——这些知识无法被协议文档、传感器数据流及现有生物医学本体所捕获。我们提出了一种可复现的结构化专家启发方法及联邦语义知识图谱架构，用于捕获和查询这类知识，并通过基因泰克公司生化与细胞药理学部门的部署进行了验证。知识通过"协议智能副驾驶"——一个专门构建的AI访谈代理——进行启发，该代理运用结构化启发视角，以专家分配的置信度评分挖掘隐性程序知识，生成涵盖三个层级的图表示：项目级决策里程碑、检测方案知识及物理执行基础设施。分别构建的子图（以免疫测定（ELISA）、定量质谱（LC-MS/PRM）及实验室自动化为典型示例）通过共享上层本体对齐，并作为单一联邦图进行查询。评估表明，存在七类无法从任何单一数据源结构上获取的查询类型，包括一项跨子图遍历——该遍历能够识别自动化掩蔽的隐性故障，即执行日志报告成功而科学有效性受损的情况。关键地，MASKED_BY 图关系编码了一类当前信息学平台无法识别的实验室风险——即阻碍现有系统推理科学有效性的结构性鸿沟。该架构为当前AI实验室代理提供了所缺乏的语义世界模型：一种可查询的表示，揭示工作流何处无声失效、何处人类判断不可替代、以及哪些执行资产掩盖而非检测故障。