面向工业数字线程的语义图统一：通过本体驱动知识图谱连接11个异构制造系统
Semantic Graph Unification for Industrial Digital Threads: Bridging 11 Heterogeneous Manufacturing Systems Through Ontology-Driven Knowledge Graphs
AI总结：
该研究提出一种本体驱动的RDF知识图谱框架，通过五阶段ETL流水线统一11个异构制造系统的数据，经实验验证其可实现跨系统信号发现，且适用于多个制造垂直领域。
AI中文摘要：
现代制造企业运行着ERP、MES、PLM、SCADA、QMS、SCM等异构系统，每个系统都有其自身的数据模型和API。由此产生的数据孤岛阻碍了整体分析，延迟了根本原因调查，并妨碍了工业4.0的可追溯性。点对点集成的扩展性为O(n^2)，且会积累脆弱的依赖关系。本文提出了一种用于工业数字线程语义图统一的开放框架。该框架采用本体驱动的RDF知识图谱，通过包含自动实体解析的五阶段ETL流水线，统一了来自9个领域的11个模拟数据源，其中自动实体解析涵盖了97个owl:sameAs身份链接。该本体包含78个RDFS类、108个对象属性和243个数据属性，参考了ISA-95、OPC UA、eClass、资产管理壳（Asset Administration Shell）、RAMI 4.0及其他标准。自动发现引擎应用9类策略——跨工位关联、报警覆盖、ECN影响、CUSUM/EWMA漂移检测——以呈现跨系统边界的洞察。主要实证结果：屏蔽24个跨系统工具会将召回率从1.00降至0.31（F1值从1.00降至0.48），表明69%的可发现信号需要跨系统图连接。留一法消融实验证实，9类策略中有6类贡献了独特信号。针对包含65个信号（16个正例、49个空例）的清单进行验证，得到F1=1.00（95% Clopper-Pearson置信区间[0.79, 1.00]）；由于该清单由作者构建，此结果属于验证而非独立验证。该图通过287个模型上下文协议（Model Context Protocol）工具作为原生SPARQL语义层暴露给大语言模型（LLM）智能体。五个行业模板（航空航天、CPG、制药、医疗器械、涡轮叶片）证明了其在不同制造垂直领域的模式稳定性。
英文摘要：
Modern manufacturing enterprises operate heterogeneous systems -- ERP, MES, PLM, SCADA, QMS, SCM -- each with its own data model and API. The resulting silos prevent holistic analysis, delay root-cause investigation, and obstruct Industry 4.0 traceability. Point-to-point integration scales as O(n^2) and accumulates brittle dependencies. This paper presents an open framework for semantic graph unification of industrial digital threads. An ontology-driven RDF knowledge graph unifies data from 11 simulated sources across nine domains through a five-stage ETL pipeline with automated entity resolution spanning 97 owl:sameAs identity links. The ontology encompasses 78 RDFS classes, 108 object properties, and 243 data properties, drawing on ISA-95, OPC UA, eClass, the Asset Administration Shell, RAMI 4.0, and additional standards. An automated discovery engine applies nine strategy categories -- cross-station correlation, alarm coverage, ECN impact, CUSUM/EWMA drift detection -- to surface insights spanning system boundaries. The primary empirical result: blocking 24 cross-system tools reduces recall from 1.00 to 0.31 (F1 from 1.00 to 0.48), showing that 69% of discoverable signals require cross-system graph joins. Leave-one-out ablation confirms six of nine strategies contribute unique signals. Verification against a 65-signal manifest (16 positive, 49 null) yields F1 = 1.00 (95% Clopper-Pearson CI [0.79, 1.00]); as the manifest was author-constructed, this constitutes verification not independent validation. The graph is exposed to LLM agents via 287 Model Context Protocol tools as a SPARQL-native semantic layer. Five industry templates (aerospace, CPG, pharma, medical devices, turbine blades) demonstrate schema stability across manufacturing verticals.
