This is an implementation of the WONKA methodology using Owlready2 and rdflib libraries, as described in our paper:

Delabeye, R., Penas, O., & Plateaux, R. (2022, October). Scalable ontology-based V&V process for heterogeneous systems and applications. In Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings (pp. 341-350).

A more detailed description of the case study is presented in the S.mart benchmark repository, where a dedicated benchmark on Early Verification and Validation is presented.

conda env create -f environment.yml
pip install -e .

• Owlready2 (> 0.41)
• rdflib
• networkx
• pandas

cd case_study
python main_MODELS2022.py

You can apply the WONKA methodology on your own data using this implementation, therefore you need to provide the following:

• an instantiated ontology (.owl file)
• a set of requirements, translated as SPARQL queries (.rq files)

The ontology should be instantiated according to the blueprint presented in the paper (from the scientific approach to be validated, to the targeted use cases). For some of the features, it is also necessary to provide the edges to remove in order to separate the sub-knowledge graphs (associated to each use case).

Please consider citing our paper if you use this code or methodology in your work:

@inproceedings{delabeye2022scalable,
  title={Scalable ontology-based V\&V process for heterogeneous systems and applications},
  author={Delabeye, Romain and Penas, Olivia and Plateaux, R{\'e}gis},
  booktitle={Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings},
  pages={341--350},
  year={2022}
}