The widespread use of knowledge graphs in various fields has brought about a challenge in effectively integrating and updating information within them. When it comes to incorporating contexts, conventional methods often rely on rules or basic machine learning models, which may not fully grasp the complexity and fluidity of context information. This research suggests an approach based on reinforcement learning (RL), specifically utilizing Deep Q Networks (DQN) to enhance the process of integrating contexts into knowledge graphs. By considering the state of the knowledge graph as environment states defining actions as operations for integrating contexts and using a reward function to gauge the improvement in knowledge graph quality post-integration, this method aims to automatically develop strategies for optimal context integration. Our DQN model utilizes networks as function approximators, continually updating Q values to estimate the action value function, thus enabling effective integration of intricate and dynamic context information. Initial experimental findings show that our RL method outperforms techniques in achieving precise context integration across various standard knowledge graph datasets, highlighting the potential and effectiveness of reinforcement learning in enhancing and managing knowledge graphs.

翻译：知识图谱在各领域的广泛应用带来了有效整合与更新其中信息的挑战。在融入上下文信息时，传统方法往往依赖规则或基础机器学习模型，难以充分把握上下文信息的复杂性与动态性。本研究提出一种基于强化学习（RL）的方法，具体采用深度Q网络（DQN）来优化知识图谱中上下文信息的集成过程。该方法将知识图谱状态视为环境状态，将动作定义为上下文集成操作，并构建奖励函数评估集成后知识图谱质量的提升幅度，旨在自主开发最优上下文集成策略。我们设计的DQN模型采用深度网络作为函数逼近器，通过持续更新Q值来估计动作价值函数，从而有效处理复杂动态的上下文信息。初步实验结果表明，在多个标准知识图谱数据集上，本强化学习方法在实现精确上下文集成方面均优于现有技术，充分彰显了强化学习在知识图谱增强与管理中的潜力和有效性。