  Efficiently inducing high-level interpretable regularities from knowledge
graphs (KGs) is an essential yet challenging task that benefits many downstream
applications. In this work, we present GPFL, a probabilistic rule learner
optimized to mine instantiated first-order logic rules from KGs. Instantiated
rules contain constants extracted from KGs. Compared to abstract rules that
contain no constants, instantiated rules are capable of explaining and
expressing concepts in more details. GPFL utilizes a novel two-stage rule
generation mechanism that first generalizes extracted paths into templates that
are acyclic abstract rules until a certain degree of template saturation is
achieved, then specializes the generated templates into instantiated rules.
Unlike existing works that ground every mined instantiated rule for evaluation,
GPFL shares groundings between structurally similar rules for collective
evaluation. Moreover, we reveal the presence of overfitting rules, their impact
on the predictive performance, and the effectiveness of a simple validation
method filtering out overfitting rules. Through extensive experiments on public
benchmark datasets, we show that GPFL 1.) significantly reduces the runtime on
evaluating instantiated rules; 2.) discovers much more quality instantiated
rules than existing works; 3.) improves the predictive performance of learned
rules by removing overfitting rules via validation; 4.) is competitive on
knowledge graph completion task compared to state-of-the-art baselines.
