Axel’s research focuses on methods to improve the life cycle of knowledge graphs with a strong focus a machine learning techniques. He has participated in/led a number of German and European H2020 projects (including HOBBIT, SAKE, GEISER, RAKI and DAIKIRI) in which he developed techniques for the extraction, integration and fusion of knowledge graphs at scale. Axel currently leads the KnowGraphs Training Network, in which early-stage researchers address some of the core challenges in the representation, extraction, management and use of knowledge graphs. Axel also leads the development of popular benchmarking frameworks such as GERBIL, IGUANA and HOBBIT. He is full professor of Data Science at Paderborn University, where he also leads the activities on Digital Humanities
Axel Ngonga
Full Professor of Data Science at Paderborn University at University of Paderborn, Lead of BDVA TF6 ...
2020 EDITION
Evaluation schemes for Big data and AI Performance of high Business impact (DataBench project sponsored session)
Wednesday Nov 4, 2020  10:00-13:30
2018 EDITION
Benchmarking Big Data (Parallel Session 3.2)
Monday Nov 12, 2018  17:00-18:30