Optimised Storage for Datalog Reasoning

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. However, storing all materialised facts may be infeasible in practice, especially when the rules are complex and the given set of facts is large. We observe that for certain combinations of rules, there exist data structures that compactly represent the reasoning result and can be efficiently queried when necessary. In this paper, we present a general framework that allows for the integration of such optimised storage schemes with standard materialisation algorithms. Moreover, we devise optimised storage schemes targeting at transitive rules and union rules, two types of (combination of) rules that commonly occur in practice. Our experimental evaluation shows that our approach significantly improves memory consumption, sometimes by orders of magnitude, while remaining competitive in terms of query answering time.

Authors

• Xinyue Zhang University of Electronic Science and Technology of China
• Pan Hu School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China
• Yavor Nenov Oxford Semantic Techonologies
• Ian Horrocks University of Oxford

Keywords

• DMKM: Semantic Web
• KRR: Logic Programming
• KRR: Ontologies