Many companies wish to maintain knowledge in the form of a corporate knowledge graph and would like to use and manage this knowledge via a knowledge graph management system (KGMS).

We introduced and discussed these concepts, giving examples, and formulating 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 discussed new knowledge-representation and reasoning formalisms and introduced a system achieving these goals. This system has been developed at Oxford as part of the VADA Value-Added Data Systems project and is currently being transferred to a new spin-out company. We also discussed some industrial applications that show how machine learning can be fruitfully combined with rule-based logical knowledge processing.

Watch Professor Georg Gottlob's Lovelace lecture