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.

这篇论文提出了一个通用框架，将优化的存储方案与标准物质化算法集成，针对常见的转换规则和联合规则，通过优化的存储方案显著改进了内存消耗，有时候提升几个数量级，同时在查询响应时间方面保持竞争力。