Fachbereich 6 Mathematik/Informatik

Institut für Informatik

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Stiftungsprofessur Semantische Informationssysteme gefördert von der ROSEN Gruppe.

Semantic Information Systems


Research Group Semantic Information Systems
Prof. Dr. Martin Atzmüller

Secretary: Jantje Apfeld
+49 541 969 2480

Semantic Information Systems
Institute of Computer Science
Osnabrueck University
P.O. Box 4469
49069 Osnabrueck, Germany


SIS Research & Mission

The research of the ROSEN-Group-Endowed Chair of Semantic Information Systems and the according research group, headed by Prof. Dr. Martin Atzmueller, centers around Artificial Intelligence and Data Science. Its major focus is on data analysis and machine learning on complex data such as graphs, networks, and temporal data, often encountered in complex systems, also with a human-centered perspective.

Overall, our work focuses on how to 'make sense' of complex information and knowledge processes - leveraging the massive amounts of data collected in science and industry by intelligent analytics and semantic interpretation. For instance, this includes the identification of interesting/exceptional patterns and structures, predictive  modeling, analysis and exploration of complex heterogeneous and multi-modal data, as well as human-centered decision support.

By connecting computational approaches with the human cognitive, behavioral, and social contextual perspectives - thus linking technologies with their users - our goal is to augment human intelligence and to assist human actors in all their purposes, both online and in the physical world.

The Semantic Information Systems research group is founding member of the Research Unit Data Science at Osnabrück University. In addition, the group is also connected with the German Research Center for Artificial Intelligence (DFKI), in particular DFKI Niedersachsen where Prof. Atzmueller is an Affiliated Professor.

People's Info

Prof. Dr. Martin Atzmueller Prof. Dr. Martin Atzmueller
Head of Semantic Information Systems group.
Research Interests:: Artificial Intelligence, Knowledge Discovery, Machine Learning, Network Science, Pattern Mining

Research Assistants/PhD Students/External PhD Students

Arnab Ghosh Chowdhury Arnab Ghosh Chowdhury
Deep Learning, Information Engineering, Multi-Modal Learning, Document Intelligence
Dan Hudson Dan Hudson
Anomaly and Exceptionality Detection, Deep Capsule Networks, Time Series Analysis, Sensor Data
Leonid Schwenke Leonid Schwenke
Deep Learning, Transformer Architectures, Knowledge-Aware Explanation Engineering, Time Series Analysis
Harihara Bharathy Swaminathan Harihara Bharathy Swaminathan
Automotive Radar Sensors, Environment Perception, HD Map Reliability, Machine Learning, Autonomous Driving.
Sophia Sylvester Sophia Sylvester
Explainability, Supervised Machine Learning, Deep Learning, Cyber-Security.
Stefan Bloemheuvel Stefan Bloemheuvel (JADS):
Graph Signal Processing, Graph Neural Networks, Time Series Analysis, Sensor Networks
Frank Ehebrecht Frank Ehebrecht (ROSEN):
Informed Machine Learning, Deep Learning, Physical Models, Sensor Data Analysis
Timo Markert Timo Markert (Wittenstein SE):
Machine Learning, Sensor Data Analysis, Tactile Object Recognition, Robotic Manipulation
Parisa Shayan Parisa Shayan (TiU):
Educational Data Mining, Network Analysis, User Modeling, Learning Management Systems
Jurgen van den Hoogen Jurgen van den Hoogen (JADS):
Deep Learning, Time Series Analysis/Classification, Fault Diagnosis, Sensor Data


  • MODUS is a project funded by DFG for Model-based Anomaly Pattern Detection and Analysis in Ubiquitous and Social Interaction Networks.

  • Di-Plast: Digital Circular Economy for the Plastics Industry (funded by Interreg NWE (EFRE, EU regional development fond)). Di-Plast improves processes for a more stable rPM material supply and quality using artificial intelligence methods and data science approaches: sensoring generates data within supply chains; data analytics provides information about rPM quality, amounts, and supply timing; Value Stream Management improves rPM processes & logistics, environmental assessments validate sustainability.

  • NWO KIEM ICT ODYN: Observing Team Dynamics and Communication using Sensor-Based Social Analytics.

  • Resilient Athletes: In this interdisciplinary project (funded by ZonMW), a multidisciplinary personalized human-sensor-based data science approach is being developed and applied. We focus on the resilience of athletes, with the aim that athletes can cope with the physical and mental stress factors to which they are exposed.


  • VIKAMINE is an extensible open-source rich-client environment and platform for exploratory pattern mining and analytics. VIKAMINE features powerful and intuitive visualizations complemented by fast automatic mining methods; it is provided as Open Source, under the GNU Lesser General Public License (LGPL).
  • The R subgroup package (rsubgroup R package) provides a wrapper around the VIKAMINE core.