MiniAMIE: Quick and Dirty Rule Mining on Knowledge Graphs

Efficient rule mining on large modern knowledge graphs (KGs) is a major challenge due to the exponential search space. Current systems -- especially those aiming for exhaustive mining -- remain resource- and time-consuming. In this paper, we propose MiniAMIE, a rule mining approach based on the AMIE algorithm, which restricts AMIE's language bias and estimates key rule metrics using fast approximations. Our experiments on several KGs illustrate the trade-offs of this design and show that MiniAMIE achieves a substantial speed-up while maintaining some good-quality rules.