  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.
