发表机构
Institute AIFB, Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院AIFB研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对RDF知识图谱无法推理物理系统动态行为的问题，提出将RDF与微分动态逻辑集成的RDFdL框架，实现静态与动态知识的统一表示推理，可用于制造业相关场景。
AI 中文摘要
以RDF建模的知识图谱在描述静态知识方面功能强大，但无法捕获或推理物理系统的动态行为，例如由微分方程描述的系统，这是AI驱动的网络物理系统的关键缺口。为解决该问题，我们提出RDFdL框架，将RDF与微分动态逻辑（dL）集成，以表示和推理静态知识及物理系统的连续动态。对于动态部分，我们在RDF和SHACL中以语法形式表示状态空间中的微分方程和范围，并通过转换为dL提供语义。通过一阶逻辑的共同基础将RDF与dL关联，实现了独特的集成：动态逻辑领域中安全性和可达性属性的验证结果，可作为对RDF数据的SPARQL查询的蕴涵结果。我们使用Apache Jena（用于本体驱动的RDF推理）和KeYmaera X（dL的定理证明器）实现该流程，并简述其在制造业中的适用性。
英文摘要
Knowledge graphs modeled in RDF are powerful for describing static knowledge, but they cannot capture or reason about the dynamic behavior of physical systems, e.g., systems described by differential equations, which is a critical gap for AI-driven cyber-physical systems. To solve this, we propose RDFdL, a framework that integrates RDF with Differential Dynamic Logic (dL) to represent and reason about both static knowledge and the continuous dynamics of physical systems. For the dynamic part, we syntactically represent differential equations and ranges in the state space in RDF and SHACL and provide semantics using a translation to dL. Linking RDF and dL through their shared foundation in first-order logic achieves a unique integration: verification results for safety and reachability properties in the dynamic logic domain become available as entailment to SPARQL queries over RDF data. We implement the pipeline using Apache Jena for ontology-driven RDF reasoning and KeYmaera X, the theorem prover for dL, and sketch its applicability in manufacturing.
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