# Integrating Ontology Design with the CRISP-DM in the context of Cyber-Physical Systems Maintenance

arXiv: https://arxiv.org/abs/2407.06930 · HTML: https://mlsanity.com/paper/2407.06930/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 9 Jul 2024.

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

- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Systematic Comparison of Software Agents and Digital Twins: Differences, Similarities, and Synergies in Industrial Production](https://mlsanity.com/paper/2307.08421/) (2023)
- [Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs](https://mlsanity.com/paper/2308.13433/) (2023)
- [Integration of Domain Expert-Centric Ontology Design into the CRISP-DM for Cyber-Physical Production Systems](https://mlsanity.com/paper/2307.11637/) (2023)

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

- [Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants](https://mlsanity.com/paper/2505.02076/) (2025)
- [Representing Time-Continuous Behavior of Cyber-Physical Systems in Knowledge Graphs](https://mlsanity.com/paper/2506.13773/) (2025)
- [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)

## Abstract (arXiv)

In the following contribution, a method is introduced that integrates domain expert-centric ontology design with the Cross-Industry Standard Process for Data Mining (CRISP-DM). This approach aims to efficiently build an application-specific ontology tailored to the corrective maintenance of Cyber-Physical Systems (CPS). The proposed method is divided into three phases. In phase one, ontology requirements are systematically specified, defining the relevant knowledge scope. Accordingly, CPS life cycle data is contextualized in phase two using domain-specific ontological artifacts. This formalized domain knowledge is then utilized in the CRISP-DM to efficiently extract new insights from the data. Finally, the newly developed data-driven model is employed to populate and expand the ontology. Thus, information extracted from this model is semantically annotated and aligned with the existing ontology in phase three. The applicability of this method has been evaluated in an anomaly detection case study for a modular process plant.