Problem
Research questions and friction points this paper is trying to address.
RDF
dynamic behavior
physical systems
differential equations
cyber-physical systems
RDFdL: Integrating RDF with Differential Dynamic Logic
🤖 AI Summary
本文提出RDFdL框架，通过将RDF与微分动态逻辑结合，解决知识图谱无法描述物理系统动态行为的问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
RDFdL
Differential Dynamic Logic
Integration of Static and Dynamic Knowledge
Verification Results as Entailment
Cyber-Physical Systems
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