Rule Mining over Knowledge Graphs via Reinforcement Learning
This addresses a bottleneck in knowledge graph applications for researchers and practitioners, but it is incremental as it builds on existing rule mining methods.
The paper tackles the inefficiency and ineffectiveness of rule mining from knowledge graphs by proposing a reinforcement learning-guided approach, achieving state-of-the-art performance in efficiency and effectiveness on several datasets.
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.