Rule Mining over Knowledge Graphs via Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based framework for efficient and effective rule mining from knowledge graphs, addressing previous limitations in rule generation and evaluation.

Contribution

It proposes a novel two-phased reinforcement learning approach for rule mining from KGs, improving both efficiency and effectiveness over existing methods.

Findings

Achieves state-of-the-art performance in rule mining tasks.

Demonstrates significant improvements in rule generation efficiency.

Shows high effectiveness in rule evaluation across multiple datasets.

Abstract

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.