Machine Learning with Knowledge Graphs
Over the past years, there has been a rapid growth in research on Machine Learning with Knowledge Graphs (KGs). KGs are large networks of real-world entities, which are described in terms of their semantic types and their relationships to each other. KGs can be represented as adjacency tensors from which tensor models can be derived to generalize to unseen facts and to derive entity embeddings. We show how Machine Learning with Knowledge Graphs can be used in industrial applications and for clinical decision support. Important issues we are addressing in the clinical setting are missing data, explainability and policy evaluation. I will discuss Machine Learning with Knowledge Graphs for rich scene graph descriptions and debate potential links to the memory and perceptual systems of the human brain.
Volker Tresp is a Distinguished Research Scientist at Siemens and a Professor for Machine Learning at the Ludwig Maximilian University of Munich (LMU). He received a Diploma degree from the University of Goettingen, Germany, in 1984 and the M.Sc. and Ph.D. degrees from Yale University, New Haven, CT, in 1986 and 1989 respectively. Since 1989 he has been the head of various research teams in Machine Learning at Siemens, Research and Technology. He filed more than 100 patent applications and was inventor of the year of Siemens in 1996. He has published more than 150 scientific articles and administered over 25 Ph.D. theses. The company Panoratio is a spin-off out of his team. His research focus in recent years has been “Machine Learning in Information Networks” for modelling Knowledge Graphs, medical decision processes, perception, and cognitive memory functions. He has been the consortium lead of a number of publicly funded projects. Since 2011 he is also a Professor at the Ludwig Maximilian University of Munich where he teaches an annual course on Machine Learning.
Back to the Summer School 2020 overview