Guest speaker from Oxford
Big Data and Enterprise Knowledge Graphs
Modern companies wish to maintain knowledge in the form of a enterprise knowledge graph and would like to use and manage this knowledge via a knowledge graph management system (KGMS). We introduce and discuss these concepts, give examples, and 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 enterprise databases, to "external data" on the web, and to machine learning and analytics. We discuss new knowledge-representation and reasoning formalisms and introduce a system achieving these goals.
This system has been developed at the University of Oxford as part of the VADA Value-Added Data Systems project and is currently being transferred to the spin-out company DeepReason.ai. We also discuss some applications that show how machine learning can be fruitfully combined with rule-based logical knowledge processing.
Dr. Emanuel Sallinger leads the VADA laboratory at the University of Oxford, UK, as senior researcher. VADA (“Value Added Data Systems”) is an EPSRC programme grant awarded to Prof. Georg Gottlob that includes the Universities of Oxford, Manchester and Edinburgh as well as a large number of industrial partners. The goal of the programme is to define the next generation of technologies for data extraction, integration, quality control, and reasoning. His research interests include data integration, artificial intelligence, in particular logic-based methods of AI, and knowledge graphs. He has been lecturer for courses at the University of Oxford and TU Wien. He was awarded his doctorate “sub auspiciis praesidentis rei publicae”. Besides his MSc and PhD in computer science, he holds an MSc in “Computer Science Management”. He is PC member for conferences such as ACM PODS and CIKM.