  Many systems have been developed in recent years to mine logical rules from
large-scale Knowledge Graphs (KGs), on the grounds that representing
regularities as rules enables both the interpretable inference of new facts,
and the explanation of known facts. Among these systems, the walk-based methods
that generate the instantiated rules containing constants by abstracting
sampled paths in KGs demonstrate strong predictive performance and
expressivity. However, due to the large volume of possible rules, these systems
do not scale well where computational resources are often wasted on generating
and evaluating unpromising rules. In this work, we address such scalability
issues by proposing new methods for pruning unpromising rules using rule
hierarchies. The approach consists of two phases. Firstly, since rule
hierarchies are not readily available in walk-based methods, we have built a
Rule Hierarchy Framework (RHF), which leverages a collection of subsumption
frameworks to build a proper rule hierarchy from a set of learned rules. And
secondly, we adapt RHF to an existing rule learner where we design and
implement two methods for Hierarchical Pruning (HPMs), which utilize the
generated hierarchies to remove irrelevant and redundant rules. Through
experiments over four public benchmark datasets, we show that the application
of HPMs is effective in removing unpromising rules, which leads to significant
reductions in the runtime as well as in the number of learned rules, without
compromising the predictive performance.
