The Vadalog System: Datalog-based Reasoning for Knowledge Graphs

TL;DR

The paper introduces the Vadalog system, a high-performance Datalog-based reasoning platform designed for complex knowledge graphs, implementing the decidable Warded Datalog+/- fragment with an innovative termination control strategy.

Contribution

It presents the first implementation of Warded Datalog+/- and demonstrates its effectiveness for reasoning over large knowledge graphs.

Findings

Achieved high-performance reasoning with Warded Datalog+/-

Validated system scalability through comprehensive experiments

Demonstrated practical applicability in knowledge graph scenarios

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 knowl\-edge-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.