Master Thesis (f/m/x) - Dynamic mapping of SysML models to knowledge graph for System Analysis Plans

The DLR Institute of Software Technology sees software as a catalyst for research and innovation. The institute's staff, currently numbering around 200, make a significant contribution to advancements in the fields of aviation, space, energy, transportation, and security through the development of state-of-the-art software solutions and innovative research.

Our areas of competence include reliable and safety-critical software systems, artificial intelligence, high-performance computing and quantum computing, human-system interaction and visualisation, software and systems engineering as well as digital platforms and digital twins.

What to expect

System Modeling Language (SysML) is widely used in Model-Based Systems Engineering (MBSE) to represent the structure, behavior, and requirements of complex systems such as space systems. During the design of space systems, engineers usually changes design parameters, with the aim of finding affordable designs. System analysis helps engineers to analyze the effect of changes in design parameters dynamically across the system. To improve the outcome of system analysis, SysML models can be transformed into knowledge graphs, with the aim of increasing data interoperability across subsystems. The goal of this thesis is to investigate techniques of mapping SysML models into knowledge graph for system representation. A prototype should be developed and evaluated across CODAD, a digital twin tool for identifying and analyzing configurations.

Your tasks

• Literature review on existing approaches for mapping SysML elements to knowledge graph
• Develop a prototype within CODAD to map sysML elements into knowledge graph automatically or semi-automatically
• Evaluate the prototype using a representative model within CODAD
• Provide recommendation and identify limitations when transforming sysML elements into knowledge graph

Your profile

• You are studying Computer Science, Systems Engineering, or a related field.
• You own good knowledge in programming, preferably in Python.
• You have good understanding of MBSE tools, such as SysML V2. 
• Furthermore, you have interest in knowledge representation, ontologies, or semantic modeling.

We look forward to getting to know you!

If you have any questions about this position (Vacancy-ID 6112) please contact:

Frank Eric Mbouga