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This research addresses a critical gap in AI-driven cyber-physical systems by unifying static knowledge graphs with the dynamic reasoning capabilities of Differential Dynamic Logic (dL). The proposed RDFdL framework enables direct verification of safety and reachability properties for physical processes described by differential equations, bridging formal logic methods with standard web data formats. By translating these logical constraints into SPARQL entailments, the work offers a practical pathway for integrating rigorous dynamic analysis into existing ontology-driven manufacturing pipelines using tools like Apache Jena and KeYmaera X.
RDFdL: Integrating RDF with Differential Dynamic Logic
arXiv:2608.18165v1 Announce Type: new 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.
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.
Artificial Intelligence (cs.AI); Formal Languages and Automata Theory (cs.FL); Logic in Computer Science (cs.LO)
arXiv:2608.18165 [cs.AI]
(or arXiv:2608.18165v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.18165 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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