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Reinforcement Learning Approach for Integrating Compressed Contexts into Knowledge Graphs

Ngoc Quach, Qi Wang, Zijun Gao, Qifeng Sun, Bo Guan, Lillian Floyd

TL;DR

This work tackles the problem of integrating condensed contexts into knowledge graphs by casting the task as a reinforcement learning problem using a Deep Q-Network (DQN). It models the knowledge graph as the environment, treats condensed-context insertions as actions, and uses rewards based on improvements in KG quality to guide learning, with the DQN approximating action values to learn effective strategies. Experimental evaluation on FB15k and WN18 shows the approach outperforms rule-based and supervised baselines across accuracy, efficiency, and overall KG quality, highlighting RL's potential for dynamic KG enrichment. The study suggests RL-based context integration can scale to evolving knowledge graphs and motivates future work with more advanced RL models and transfer learning for KG management.

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.

Reinforcement Learning Approach for Integrating Compressed Contexts into Knowledge Graphs

TL;DR

This work tackles the problem of integrating condensed contexts into knowledge graphs by casting the task as a reinforcement learning problem using a Deep Q-Network (DQN). It models the knowledge graph as the environment, treats condensed-context insertions as actions, and uses rewards based on improvements in KG quality to guide learning, with the DQN approximating action values to learn effective strategies. Experimental evaluation on FB15k and WN18 shows the approach outperforms rule-based and supervised baselines across accuracy, efficiency, and overall KG quality, highlighting RL's potential for dynamic KG enrichment. The study suggests RL-based context integration can scale to evolving knowledge graphs and motivates future work with more advanced RL models and transfer learning for KG management.

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.
Paper Structure (12 sections, 3 equations, 2 figures, 3 tables)

This paper contains 12 sections, 3 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Prior Work
  3. Traditional Methods for Knowledge Graph Enrichment
  4. Enhancing Knowledge Graphs with Machine Learning
  5. Reinforcement Learning for Dynamic Data Integration
  6. Methodology
  7. DQN Architecture
  8. Experiments
  9. Experimental Setup
  10. Datasets
  11. Main Result
  12. Conclusion

Figures (2)

  • Figure 1: preformance Variance
  • Figure 2: Knowledge Graph Quality Improvement