Description

Problem: 

Simulation-based Digital Twins (DTs) often produce complex formal models, such as Stochastic Petri Nets (SPNs), that are difficult for non-expert stakeholders to interpret. Without structured semantic representations, model elements remain opaque, limiting explainability, model validation, trust, and actionable decision-making. Developing ontologies to semantically annotate these models is necessary to bridge this interpretability gap.

Goal: 

To design, develop, and implement an ontology and demonstrate its integration in a small-scale DT simulation, evaluating coverage, usability, and interpretability.

Required Skills and Knowledge: 

• Understanding of Digital Twins and simulation models (basic knowledge of Petri nets or DES is helpful).
• Knowledge of ontologies and semantic technologies (RDF, OWL, Protégé).
• Knowledge of Python programming language.
• knowledge engineering: designing, structuring, and formalizing knowledge.
• Ability to document and evaluate design decisions.