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He is researching and developing methods and algorithms at the confluence of Data Science, Artificial Intelligence and Mathematics. He is interested in the development of methods for the semantic and structural analysis of (social) networks and for the discovery of concept hierarchies. In the last years, Gerd Stumme has worked in the research areas Semantic Web, Web Mining, Social Bookmarking systems, and recommender systems. Recently, he is returning to his mathematical roots [245], studying how to exploit mathematical structures (in particular graphs and ordered sets) for knowledge acquisition and knowledge communication.
He established his group, the Knowledge & Data Engineering Group at the University of Kassel, as endowed chair of the Hertie Foundation in 2004. The group is/has been running several web platforms as online laboratory for evaluating its algorithms: the social bookmark and publication sharing system BibSonomy with 1.7 million registered users and 2.6 million accesses per day, the naming platform, the Conferator and MyGroup platforms for gathering social interaction at meetings and in working groups, as well as the platforms Widenoise and Airprobe for the worldwide detection of noise pollution and air quality.
He established his group, the Knowledge & Data Engineering Group at the University of Kassel, as endowed chair of the Hertie Foundation in 2004. The group is/has been running several web platforms as online laboratory for evaluating its algorithms: the social bookmark and publication sharing system BibSonomy with 1.7 million registered users and 2.6 million accesses per day, the naming platform, the Conferator and MyGroup platforms for gathering social interaction at meetings and in working groups, as well as the platforms Widenoise and Airprobe for the worldwide detection of noise pollution and air quality.
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INFORMATION SCIENCES (2024): 120009
CoRR (2023): 138-152
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Transactions on Graph Data and Knowledgeno. 1 (2023): 6:1-6:39
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES: RESEARCH TRACK, ECML PKDD 2023, PT III (2023): 177-192
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