We are a team of Computer Scientists from the University of Oxford with world-class research expertise in AI and the commercial acumen to deliver on our ambitious roadmap.
Why cutting-edge Knowledge Graphs need to combine Logical Reasoning with Machine Learning to tackle the hard problems of enterprises. And an overview of DeepReason.ai’s system.
Title: Swift Logic for Big Data and Knowledge Graphs
Abstract: “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 inter- faces to corporate databases, the web, and machine- learning and analytics packages. We present KRR formalisms and a system achieving these goals.”
Learn more about the technological innovation, in our 2018 system paper in the leading database conference.
Title: The Vadalog System – Datalog-based Reasoning for Knowledge Graphs
Abstract: “Over the past years, there has been a resurgence of Datalog-based
systems in the database community as well as in industry. In this context, it has been recognized that to handle the complex knowledge-based scenarios encountered today, such as reasoning over large knowledge graphs, Datalog has to be extended with features such as existential quantification. Yet, Datalog-based reasoning in the presence of existential quantification is in general undecidable. Many efforts have been made to define decidable fragments. Warded Datalog+/- is a very promising one, as it captures PTIME complexity while allowing ontological reasoning. Yet so far, no implementation of Warded Datalog+/- was available. In this paper we present the Vadalog system, a Datalog-based system for performing complex logic reasoning tasks, such as those required in advanced knowledge graphs. The Vadalog system is Oxford’s contribution to the VADA research programme, a joint effort of the universities of Oxford, Manchester and Edinburgh and around 20 industrial partners. As the main contribution of this paper, we illustrate the first implementation of Warded Datalog+/-, a high-performance Datalog+/- system utilizing an aggressive termination control strategy. We also provide a comprehensive experimental evaluation.”
Title: Data Wrangling for Big Data: Towards a Lingua Franca for Data Wrangling