About our working group
Brief presentation of our topics
“We transform data into information and then into knowledge: by combining graph-based structures with artificial intelligence methods, we make knowledge accessible where it is needed most—right at the point of care. In this way, we support medicine, healthcare, and education with intelligent systems.”
Medical Informatics and Graph-Based Systems
Our research profile
The Medical Informatics and Graph-based Systems group at the University of Siegen (MIGS) combines expertise in the fields of knowledge management, artificial intelligence, data and text mining, machine learning and semantic technologies/knowledge graphs and the use of sensor data and intelligent measurement systems. The focus of our research is the creation of new approaches to knowledge modeling with knowledge graphs and their use for hybrid, explainable recommendation systems that explore knowledge structures and make knowledge usable across systems. We support the domains of medicine, education and industrial production and maintenance. The focus is always on people and the question of how knowledge can be made usable for intelligent systems and how this can be implemented using knowledge structures and artificial intelligence methods.
Research priorities
- Digital telematics infrastructure
- Sensor data and smart metering systems
- Artificial intelligence
- Data and text mining
- Natural language processing and machine learning
- Knowledge graphs and semantic technologies
- Knowledge modeling
- Explainable recommendation systems
Latest publications
A selection of our current publications. Follow the link to the full list for a complete overview.
A Knowledge Graph-Based Approach for Personalized Course and Curriculum Path Recommendation
A Knowledge Graph-Based Approach for Personalized Course and Curriculum Path Recommendation
Evaluation of Real-Time Preprocessing Methods in AI-Based ECG Signal Analysis
Evaluation of Real-Time Preprocessing Methods in AI-Based ECG Signal Analysis
LLM-Assisted Knowledge Graph Completion for Curriculum and Domain Modelling in Personalized Higher Education Recommendations
LLM-Assisted Knowledge Graph Completion for Curriculum and Domain Modelling in Personalized Higher Education Recommendations
A comprehensive review of digital twin in healthcare in the scope of simulative health-monitoring
A comprehensive review of digital twin in healthcare in the scope of simulative health-monitoring
Contact the working group
Postal address
University of Siegen
Working group .MIGS
Am Eichenhang 50
57076 Siegen
Visitor address
University of Siegen
Artur-Woll-Haus
AE-C Level 2
Am Eichenhang 50
57076 Siegen
Secretariat
If you have any questions, comments or contact requests, please get in touch with one of the team by e-mail.