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
More about Prof Georg Gottlob
Georg Gottlob is a Professor of Informatics at Oxford University and at TU Wien. His interests include knowledge representation, logic and complexity, problem decompositions, and, on the more applied side, web data extraction, and database query processing.
He has received the Wittgenstein Award from the Austrian National Science Fund, is an ACM Fellow, an ECCAI Fellow, a Fellow of the Royal Society, and a member of the Austrian Academy of Sciences, the German National Academy of Sciences, and the Academia Europaea. He chaired the Program Committees of IJCAI 2003 and ACM PODS 2000.
He was the main founder of Lixto, a company that provides tools and services for semi-automatic web data extraction which was acquired by McKinsey & Company in 2013. Gottlob was awarded an ERC Advanced Investigator's Grant for the project "DIADEM: Domain-centric Intelligent Automated Data Extraction Methodology". Based on results of this project, he co-founded Wrapidity Ltd, a company that specializes in fully automated web data extraction that was recently acquired by Meltwater.