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