Efficient data structures for fast and low-cost first-order logic rule mining

Logic rule mining discovers association patterns in the form of logic rules from structured data. Logic rules are widely applied in information systems to assist decisions in an interpretable way. However, too many computational resources are required in state-of-the-art systems, as most of these systems optimize rule mining algorithms from the perspectives of algorithms and architecture, while data efficiency has been overlooked. Although some start-of-the-art systems implement customized data structures to improve mining speed, the space overhead of the data structures is unaffordable when processing large-scale knowledge bases. Therefore, in this article, we propose data structures to improve data efficiency and accelerate logic rule mining. Our techniques implicitly represent the Cartesian product of variable substitutions in logic rules and build compact indices for a logic entailment cache. Furthermore, we create a pool and a lookup table for the cache so that cache components will not be repeatedly created. The evaluation results show that over 95% of memory can be reduced by our techniques, and mining procedures have been accelerated by about 20x on average. Most importantly, mining on large-scale knowledge bases is practical on normal hardware where only one thread and 20GB of memory are sufficient even for large-scale knowledge bases. • We optimize logic rule mining from the perspective of data efficiency. • Inducing first-order logic rules from large-scale KBs is practical with normal PC hardwares. • Cache fragments implicitly represent Cartesian product of variable substitutions. • Integer tables build compact and efficient index for rule mining cache. • Cache components are pooled to avoid duplications.