The Vadalog System: Datalog-based Reasoning for Knowledge Graphs

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.Comment: Extended version of VLDB paper <https://doi.org/10.14778/3213880.3213888

QL: Object-oriented Queries on Relational Data

This paper describes QL, a language for querying complex, potentially recursive data structures. QL compiles to Datalog and runs on a standard relational database, yet it provides familiar-looking object-oriented features such as classes and methods, reinterpreted in logical terms: classes are logical properties describing sets of values, subclassing is implication, and virtual calls are dispatched dynamically by considering the most specific classes containing the receiver. Furthermore, types in QL are prescriptive and actively influence program evaluation rather than just describing it. In combination, these features enable the development of concise queries based on reusable libraries, which are written in a purely declarative style, yet can be efficiently executed even on very large data sets. In particular, we have used QL to implement static analyses for various programming languages, which scale to millions of lines of code