With rapidly rising unemployment rates, it has become clear that the economic effect of COVID-19 has impacted many people's lives. Yet the economic problems of one company do not only have an effect on its own employees and shareholders, we know that companies form a complex network defined by a variety of relationships, among them ownership and control. Do these intricate relationships of companies lead to resilience, that is the power to resist crises like we are observing right now, or do they actually lead to increased risk?

Three of our papers were accepted at the International Joint Conference on Rules and Reasoning (RuleML+RR), part of Declarative AI 2020. They are on Knowledge Graphs for Anti-Money Laundering, COVID-19 and Uncertain Knowledge. We are happy to have received the best presentation award for Reasoning under Uncertainty in Datalog+/- Knowledge Graphs.

We are happy to announce that our new open access book on Knowledge Graphs and Big Data Processing has been published. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for Knowledge Graphs and big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. The book is released in collaboration with the EU H2020 LAMBDA initiative, including Fraunhofer IAIS, the University of Bonn, Institute Mihajlo Pupin, and the University of Oxford. It is available as an open access Springer LNCS publication.