GSBRL: Efficient RDF graph storage based on reinforcement learning

Knowledge is the cornerstone of artificial intelligence, which is often represented as RDF graphs. The large-scale RDF graphs in various fields pose new challenges to graph data management. Due to the maturity and stability, relational database is a good choice for RDF graph storage. However, the management of the complex structure of RDF graphs in the relational database requires sophisticated storage structure design. To address this problem, this paper adopts reinforcement learning (RL) to optimize the storage partition method of RDF graph. To the best of our knowledge, this is the first work to adopt RL to solve this problem. Moreover, we propose the featurization method of RDF tables which guarantees adequacy of state representation and the query rewriting policy which ensures correct query results when the storage structure changes. Extensive experiments on various RDF benchmarks demonstrate that the proposed approach significantly outperforms the state-of-the-art storage strategies.