Team

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.

Yavor Nenov

Senior Research Engineer

Evgeny Sherkhonov

Senior Research Engineer

Ruslan Fayzrakhmanov

Senior Research Engineer

Gerald Berger

Research Engineer

We are hiring

Join our team in Central Oxford

Publications

Our Vision for a Modern Knowledge Graph – IJCAI 2017 Keynote talk

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.”

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Watch keynote video

Overview of our Technology – Published in VLDB 2018

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.”

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Reasoning for Data Wrangling - AMW 2017 Conference

Title: Data Wrangling for Big Data: Towards a Lingua Franca for Data Wrangling

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