Reinforcement Learning Approach for Integrating Compressed Contexts into Knowledge Graphs

TL;DR

This paper introduces a reinforcement learning approach using Deep Q Networks to improve the integration of complex and dynamic contexts into knowledge graphs, outperforming traditional methods.

Contribution

It presents a novel RL-based framework for context integration in knowledge graphs, leveraging DQN to optimize strategies automatically.

Findings

RL method outperforms traditional techniques

Effective integration of complex contexts demonstrated

Improves knowledge graph quality

Abstract

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.