Arbeitsgruppe Semantische Informationssysteme
Research & Mission
The research of the research group Semantic Information Systems, headed by Prof. Dr. Martin Atzmueller, centers around Artificial Intelligence (AI), Data Science, and Integrative AI Systems. Its major focus is on machine learning and analysis on complex (sensor) data such as images, graphs, networks, and temporal data, often encountered in complex systems, as well as the respective system view and design perspectives.
Overall, our research focuses on how to 'make sense' of complex information and knowledge processes - supporting intelligent decision making and according acting - by leveraging the massive amounts of data collected in science and industry. This includes research on modeling complex data, explainable AI, interpretable learning, machine perception as well as semantic interpretation. In particular, this also relates to applications in complex integrative AI system domains, for example, to robot control and integrative sensor-based AI systems.
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 Scientific Director of the Research Department Cooperative and Autonomous Systems.
In addition, Prof. Atzmueller is founding spokesperson of the Joint Lab on Artificial Intelligence and Data Science and the HybrInt research group (Hybrid Intelligence through Interpretable AI in Machine Perception and Interaction).
Kontakt
Research Group Semantic Information Systems
Prof. Dr. Martin Atzmüller
Secretary: Jantje Apfeld
sekretariat@informatik.uni-osnabrueck.de
+49 541 969 2480
Semantic Information Systems
Institute of Computer Science
Osnabrueck University
P.O. Box 4469
49069 Osnabrueck, Germany
News
- New paper in Data Mining and Knowledge Discovery
Martin Atzmueller, Johannes Fürnkranz, Tomas Kliegr, Ute Schmid (2024) Explainable and Interpretable Machine Learning and Data Mining - New paper in ROMAN (IEEE International Conference on Robot and Human Interactive Communication)
Timo Markert, Sebastian Matich, Daniel Neykov, Jonas Pfannes, Andreas Theissler, Martin Atzmueller (2024) Force-based Haptic Input Device and Online Motion Generator: Investigating Learning Curves in Robotic Telemanipulation - New paper in International Journal of Data Science and Analytics
Stefan Bloemheuvel, Jurgen van den Hoogen, and Martin Atzmueller (2023) Graph construction on complex spatiotemporal data for enhancing graph neural network-based approaches - New paper in International Journal of Data Science and Analytics
Jurgen van den Hoogen, Dan Hudson, Stefan Bloemheuvel, Martin Atzmueller (2023) Hyperparameter analysis of wide-kernel CNN architectures in industrial fault detection: an exploratory study - New paper in Data Mining and Knowledge Discovery
Mouloud Iferroudjene, Corentin Lonjarret, Celine Robardet, Marc Plantevit, Martin Atzmueller (2023) Methods for Explaining Top-N Recommendations Through Subgroup Discovery - Best Paper Award (IEEE ETFA/IES Young Professionals & Students Paper Award) for the Paper Visual Detection of Tiny and Transparent Objects for Autonomous Robotic Pick-and-Place Operations, IEEE (2022) by Timo Markert, Sebastian Matich, Daniel Neykov, Markus Muenig, Andreas Theissler, and Martin Atzmueller
>> Check out our video << - New paper in International Journal of Data Science and Analytics
Stefan Bloemheuvel, Jurgen van den Hoogen, Dario Jozinovic, Alberto Michelini, and Martin Atzmueller (2022) Graph neural networks for multivariate time series regression with application to seismic data - New paper Behavior Research Methods
Dan Hudson, Travis J. Wiltshire, and Martin Atzmueller (2022) multiSyncPy: A Python Package for Assessing Multivariate Coordination Dynamics - New paper in Applied Network Science.
Stefan Bloemheuvel, Jurgen van den Hoogen and Martin Atzmueller (2021) A Computational Framework for Modeling Complex Sensor Network Data Using Graph Signal Processing and Graph Neural Networks in Structural Health Monitoring - New Paper in Applied Sciences: Special Issue Data Mining Applications in Industry 4.0
Jurgen van den Hoogen, Stefan Bloemheuvel, and Martin Atzmueller (2021) Classifying Multivariate Signals in Rolling Bearing Fault Detection Using Adaptive Wide-Kernel CNNs - Best Paper Award (IEEE ETFA/IES Young Professionals & Students Paper Award) for the Paper Fingertip 6-Axis Force/Torque Sensing for Texture Recognition in Robotic Manipulation, IEEE (2021) by Timo Markert, Sebastian Matich, Elias Hoerner, Andreas Theissler, and Martin Atzmueller
>> Check out our video << - New Paper accepted at DSAA 2021 (8th IEEE International Conference on Data Science and Advanced Analytics) Leonid Schwenke and Martin Atzmueller (2021) Constructing Global Coherence Representations: Identifying Interpretability and Coherences of Transformer Attention in Time Series Data
- New Paper - nominated for Best Paper Award - at the 34th International FLAIRS Conference.
Leonid Schwenke and Martin Atzmueller (2021) Show Me What You’re Looking For: Visualizing Abstracted Transformer Attention for Enhancing Their Local Interpretability on Time Series Data.