Rational preferential reasoning for datalog

Datalog is a powerful language that can be used to represent explicit knowledge and compute inferences in knowledge bases. Datalog cannot represent or reason about contradictory rules, though. This is a limitation as contradictions are often present in domains that contain exceptions. In this paper, we extend datalog to represent contradictory and defeasible information. We define an approach to efficiently reason about contradictory information in datalog and show that it satisfies the KLM requirements for a rational consequence relation. Finally, we introduce an implementation of this approach in the form of a defeasible datalog reasoning tool and evaluate the performance of this tool

HEX Programs with Action Atoms

HEX programs were originally introduced as a general framework for extending declarative logic programming, under the stable model semantics, with the possibility of bidirectionally accessing external sources of knowledge and/or computation. The original framework, however, does not deal satisfactorily with stateful external environments: the possibility of predictably influencing external environments has thus not yet been considered explicitly. This paper lifts HEX programs to ACTHEX programs: ACTHEX programs introduce the notion of action atoms, which are associated to corresponding functions capable of actually changing the state of external environments. The execution of specific sequences of action atoms can be declaratively programmed. Furthermore, ACTHEX programs allow for selecting preferred actions, building on weights and corresponding cost functions. We introduce syntax and semantics of acthex programs; ACTHEX programs can successfully be exploited as a general purpose language for the declarative implementation of executable specifications, which we illustrate by encodings of knowledge bases updates, action languages, and an agent programming language. A system capable of executing ACTHEX programs has been implemented and is publicly available

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