Knowledge graphs have recently gained popularity as sources of factual knowledge, represented as graphs, that can be used for a variety of applications. Knowledge graphs are the ultimate data set, encoding facts with well-defined semantics modeled in an ontology. Ultimately, knowledge graphs are to AI what explicit factual memory is to human intelligence: a way to organize knowledge about the world.
In this presentation, Dr. François Scharffe will give an overview of current applications of knowledge graphs. He will then explain the basics of representing, storing and querying data in a knowledge graph.
Dr. François Scharffe is a scholar and a practitioner working on knowledge representation for artificial intelligence. As a scholar, he tries to understand how factual knowledge can be better processed to make machines smarter. As an entrepreneur, he incorporates these techniques in AI-driven companies. François holds a PhD from the University of Innsbruck, Austria. He is currently a Senior Lecturer at Columbia University's School of Professional Studies. He is also the Director of the Data Chefs, a consulting agency specialized in Knowledge Graphs.
François is the chair of the Knowledge Graph Conference, to be held at Columbia University on May 7 and 8th.