# Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs

arXiv: https://arxiv.org/abs/2308.13433 · HTML: https://mlsanity.com/paper/2308.13433/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 25 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [SDM-RDFizer: An RML Interpreter for the Efficient Creation of RDF Knowledge Graphs](https://mlsanity.com/paper/2008.07176/) (2020)
- [Integration of Domain Expert-Centric Ontology Design into the CRISP-DM for Cyber-Physical Production Systems](https://mlsanity.com/paper/2307.11637/) (2023)
- [Accessing and Interpreting OPC UA Event Traces based on Semantic Process Descriptions](https://mlsanity.com/paper/2207.12252/) (2022)

## Built on by (most cited first, 5 of 5)

- [Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants](https://mlsanity.com/paper/2505.02076/) (2025)
- [Integrating Ontology Design with the CRISP-DM in the context of Cyber-Physical Systems Maintenance](https://mlsanity.com/paper/2407.06930/) (2024)
- [Representing Time-Continuous Behavior of Cyber-Physical Systems in Knowledge Graphs](https://mlsanity.com/paper/2506.13773/) (2025)
- [KAPPS: A knowledge-based CPPS Architecture for the Circular Factory](https://mlsanity.com/paper/2605.22457/) (2026)
- [Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2606.31614/) (2026)

## Abstract (arXiv)

Model-Based Anomaly Detection has been a successful approach to identify deviations from the expected behavior of Cyber-Physical Production Systems. Since manual creation of these models is a time-consuming process, it is advantageous to learn them from data and represent them in a generic formalism like timed automata. However, these models - and by extension, the detected anomalies - can be challenging to interpret due to a lack of additional information about the system. This paper aims to improve model-based anomaly detection in CPPS by combining the learned timed automaton with a formal knowledge graph about the system. Both the model and the detected anomalies are described in the knowledge graph in order to allow operators an easier interpretation of the model and the detected anomalies. The authors additionally propose an ontology of the necessary concepts. The approach was validated on a five-tank mixing CPPS and was able to formally define both automata model as well as timing anomalies in automata execution.