Optimised Storage for Datalog Reasoning

no code implementations • 18 Dec 2023 • Xinyue Zhang, Pan Hu, Yavor Nenov, Ian Horrocks

Materialisation facilitates Datalog reasoning by precomputing all consequences of the facts and the rules so that queries can be directly answered over the materialised facts.

Enhancing Datalog Reasoning with Hypertree Decompositions

no code implementations • 11 May 2023 • Xinyue Zhang, Pan Hu, Yavor Nenov, Ian Horrocks

In this paper, we provide algorithms that exploit hypertree decompositions for the materialisation and incremental evaluation of Datalog programs.

Data Science with Vadalog: Bridging Machine Learning and Reasoning

no code implementations • 23 Jul 2018 • Luigi Bellomarini, Ruslan R. Fayzrakhmanov, Georg Gottlob, Andrey Kravchenko, Eleonora Laurenza, Yavor Nenov, Stephane Reissfelder, Emanuel Sallinger, Evgeny Sherkhonov, Lianlong Wu

Following the recent successful examples of large technology companies, many modern enterprises seek to build knowledge graphs to provide a unified view of corporate knowledge and to draw deep insights using machine learning and logical reasoning.

BIG-bench Machine Learning Knowledge Graphs +2

Handling owl:sameAs via Rewriting

no code implementations • 13 Nov 2014 • Boris Motik, Yavor Nenov, Robert Piro, Ian Horrocks

Rewriting is widely used to optimise owl:sameAs reasoning in materialisation based OWL 2 RL systems.

Datalog Rewritability of Disjunctive Datalog Programs and its Applications to Ontology Reasoning

no code implementations • 11 Apr 2014 • Mark Kaminski, Yavor Nenov, Bernardo Cuenca Grau

We study the problem of rewriting a disjunctive datalog program into plain datalog.