Swift Logic for Big Data and Knowledge Graphs.

Many modern companies wish to maintain knowledge in the form of a corporate knowledge graph and to use and manage this knowledge via a knowledge graph management system (KGMS). We formulate various requirements for a fully-fledged KGMS. In particular, such a system must be capable of performing complex reasoning tasks but, at the same time, achieve efficient and scalable reasoning over Big Data with an acceptable computational complexity. Moreover, a KGMS needs interfaces to corporate databases, the web, and machine-learning and analytics packages. We present KRR formalisms and a system achieving these goals. To this aim, we use specific suitable fragments from the Datalog(^pm ) family of languages, and we introduce the vadalog system, which puts these swift logics into action. This system exploits the theoretical underpinning of relevant Datalog(^pm ) languages and combines it with existing and novel techniques from database and AI practice.