A Dual-Store Structure for Knowledge Graphs

TL;DR

This paper introduces a dual-store structure combining relational and graph stores, using reinforcement learning to adaptively optimize data transfer, significantly improving query performance for large-scale knowledge graphs.

Contribution

It proposes a novel dual-store architecture with an adaptive data transfer mechanism based on reinforcement learning, enhancing knowledge graph query efficiency.

Findings

Achieves up to 43.72% improvement in query performance.

Effectively manages large-scale knowledge graphs with dynamic workloads.

Demonstrates the effectiveness of reinforcement learning in storage management.

Abstract

To effectively manage increasing knowledge graphs in various domains, a hot research topic, knowledge graph storage management, has emerged. Existing methods are classified to relational stores and native graph stores. Relational stores are able to store large-scale knowledge graphs and convenient in updating knowledge, but the query performance weakens obviously when the selectivity of a knowledge graph query is large. Native graph stores are efficient in processing complex knowledge graph queries due to its index-free adjacent property, but they are inapplicable to manage a large-scale knowledge graph due to limited storage budgets or inflexible updating process. Motivated by this, we propose a dual-store structure which leverages a graph store to accelerate the complex query process in the relational store. However, it is challenging to determine what data to transfer from relational store to graph store at what time. To address this problem, we formulate it as a Markov Decision Process and derive a physical design tuner DOTIL based on reinforcement learning. With DOTIL, the dual-store structure is adaptive to dynamic changing workloads. Experimental results on real knowledge graphs demonstrate that our proposed dual-store structure improves query performance up to average 43.72% compared with the most commonly used relational stores.