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Machine Learning and Artificial Intelligence Lab



Identifying Commonsense Spatial Knowledge via Simulating Spatial Jokes using Blender.
by Bitasta Biswas and Tiansi Dong
At  the 3rd Panel on Humor and Artificial Intelligence, 2022 International Society of Humor Studies Conference



New Paper Out: Till Schulz, Pascal Welke, and Stefan Wrobel: Graph Filtration Kernels at AAAI 2022.

New Paper out:

Hailong Jin, Tiansi Dong, Lei Hou, Juanzi Li, et al.

How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing?

Findings in ACL 2022.


New Paper Out: Till Schulz, Tamas Horvath, Pascal Welke, Stefan Wrobel: A Generalized Weisfeiler Lehman Graph Kernel. Machine Learning Journal, Springer.



New paper out:
Learning Deep Generative Models for Queuing Systems
Cesar Ojeda, Kostadin Cvejosky, Bogdan Georgiev, Christian Bauckhage, Jannis Schuecker and Ramses J. Sanchez
at AAAI 2021.


New Paper out:

Zijun Yao, Chengjiang Li, Tiansi Dong, Xin Lv, Jifan Yu, Lei Hou,  Juanzi Li, Yichi Zhang, Zelin Dai:

Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making

at ACL-IJCNLP 2021.


New paper out:

Janis Kalofolias, Pascal Welke, Jilles Vreeken: SUSAN: The Structural Similarity Random Walk Kernel at SDM 2021

Welcome to the Machine Learning and Artificial Intelligence Lab. Our group is part of the Chair of Intelligent Analysis and Information Systems (Prof. Dr. Stefan Wrobel) and jointly led by Prof. Dr. Stefan Wrobel and Prof. Dr. Christian Bauckhage.

Our group focuses on the neighboring subfields of computer science known as machine learning (ML), artificial intelligence (AI), and knowledge discovery in databases (KDD, sometimes referred to simply as data mining).  For us, these fields include on the one hand the automated analysis of large data sets using intelligent algorithms that are capable of extracting from the collected data hidden knowledge in order to produce models that can be used for prediction and decision making.  On the other hand, they also include algorithms and systems that are capable of learning from experience and adapting to their environment or their users.

Given the enormous growth of collected and available data in companies, industry and science, techniques for analyzing such data are becoming ever more important.  Today, data to be analyzed are no longer restricted to sensor data and classical databases, but more and more include textual documents and webpages (text mining, Web mining), spatial data, multimedia data, relational data (molecules, social networks).

Research in knowledge discovery and machine learning combines classical questions of computer science (efficient algorithms, software systems, databases) with elements from artificial intelligence and statistics up to user oriented issues (visualization, interactive mining). In our work, we strive to combine theoretical and technical advances in research with real-world applications to show that things really work.

Our group is part of the Chair of Intelligent Analysis and Information Systems (Prof. Dr. Stefan Wrobel) and thus carried by two institutions, namely the computer science department of the University of Bonn, where we are part of Informatik III, and Fraunhofer IAIS, the Fraunhofer Institute for intelligent analysis and information systems, where Prof. Wrobel is also director. 

To find out about our group members, research, publications and teaching, please click on the menu items in the menu on top. If you are interested in working with us as a PhD student or postdoc, please send an email with the usual material and a brief statement of your research interest to Martina Doelp.

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