This seminar's focus is on knowledge graphs (KG) in general and on three common knowlege graph problems in particular. We will learn about KG construction, KG completion using embeddings, and KG correction based on current state-of-the-art deep learning approaches. In the first half of the semester, each student will read and present one paper solving one of the three tasks. In the second half, the students will work in teams of three to create/complete/correct a knowledge graph. We collaborate with the Wildenstein Plattner Institute in New York, which provides training data, namely a large collection of art-historic documents out of which a KG should be created.
