.png){kind=icon}
返回
Rule mining over knowledge graphs via reinforcement learning
.png){kind=icon}
OA
AI
DOI：10.1016/j.knosys.2022.108371.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
Keyword:
Rule mining
Reinforcement learning
Representation learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Kinematic analysis of limb movements in neuropsychological research: Subtle deficits and recovery of function.神经心理学研究中肢体运动的运动学分析: 细微的缺陷和功能的恢复。
.png){kind=icon}
PREAI
分享收藏ADRL: An attention-based deep reinforcement learning framework for knowledge graph reasoningADRL: 一种基于注意力的深度强化学习知识图推理框架
PREAI
分享收藏分享收藏Fabrication of porous hollow γ-Al2O3 nanofibers by facile electrospinning and its application for water remediation静电纺丝法制备多孔中空 γ-Al2O3纳米纤维及其在水体修复中的应用
PREAI
分享收藏Deep reinforcement learning for transportation network combinatorial optimization: A survey交通网络组合优化的深度强化学习研究综述
PREAI
分享收藏
查看更多内容