# Representing Time-Continuous Behavior of Cyber-Physical Systems in Knowledge Graphs

arXiv: https://arxiv.org/abs/2506.13773 · HTML: https://mlsanity.com/paper/2506.13773/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 8 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs](https://mlsanity.com/paper/2308.13433/) (2023)
- [Integrating Ontology Design with the CRISP-DM in the context of Cyber-Physical Systems Maintenance](https://mlsanity.com/paper/2407.06930/) (2024)
- [Integration of Domain Expert-Centric Ontology Design into the CRISP-DM for Cyber-Physical Production Systems](https://mlsanity.com/paper/2307.11637/) (2023)
- [An Ontological Metamodel for Cyber-Physical System Safety, Security, and Resilience Coengineering](https://mlsanity.com/paper/2006.05304/) (2020)
- [Physics-based simulation ontology: an ontology to support modelling and reuse of data for physics-based simulation](https://mlsanity.com/paper/2503.11723/) (2025)

## Built on by (most cited first, 4 of 4)

- [From Detection to Action: Using LLM Agents for Fault-Tolerant Control](https://mlsanity.com/paper/2606.28011/) (2026)
- [Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2606.31614/) (2026)
- [A Tutorial on Autonomous Fault-Tolerant Control Using Knowledge-Grounded LLM Agents](https://mlsanity.com/paper/2606.31635/) (2026)
- [XPhysICS: Cross-Physical-Domain Threat Grounding for Industrial Control Systems Security](https://mlsanity.com/paper/2609.30805/) (2026)

## Abstract (arXiv)

Time-continuous dynamic models are essential for various Cyber-Physical System (CPS) applications. To ensure effective usability in different lifecycle phases, such behavioral information in the form of differential equations must be contextualized and integrated with further CPS information. While knowledge graphs provide a formal description and structuring mechanism for this task, there is a lack of reusable ontological artifacts and methods to reduce manual instantiation effort. Hence, this contribution introduces two artifacts: Firstly, a modular semantic model based on standards is introduced to represent differential equations directly within knowledge graphs and to enrich them semantically. Secondly, a method for efficient knowledge graph generation is presented. A validation of these artifacts was conducted in the domain of aviation maintenance. Results show that differential equations of a complex Electro-Hydraulic Servoactuator can be formally represented in a knowledge graph and be contextualized with other lifecycle data, proving the artifacts' practical applicability.