  Knowledge graphs (KGs) are an important source repository for a wide range of
applications and rule mining from KGs recently attracts wide research interest
in the KG-related research community. Many solutions have been proposed for the
rule mining from large-scale KGs, which however are limited in the inefficiency
of rule generation and ineffectiveness of rule evaluation. To solve these
problems, in this paper we propose a generation-then-evaluation rule mining
approach guided by reinforcement learning. Specifically, a two-phased framework
is designed. The first phase aims to train a reinforcement learning agent for
rule generation from KGs, and the second is to utilize the value function of
the agent to guide the step-by-step rule generation. We conduct extensive
experiments on several datasets and the results prove that our rule mining
solution achieves state-of-the-art performance in terms of efficiency and
effectiveness.
