  Knowledge graph is an important cornerstone of artificial intelligence. The
construction and release of large-scale knowledge graphs in various fields pose
new challenges to knowledge graph data management. Due to the maturity and
stability, relational database is also suitable for RDF data storage. However,
the complex structure of RDF graph brings challenges to storage structure
design for RDF graph in the relational database. To address the difficult
problem, this paper adopts reinforcement learning (RL) to optimize the storage
partition method of RDF graph based on the relational database. We transform
the graph storage into a Markov decision process, and develop the reinforcement
learning algorithm for graph storage design. For effective RL-based storage
design, we propose the data feature extraction method of RDF tables and the
query rewriting priority policy during model training. The extensive
experimental results demonstrate that our approach outperforms existing RDF
storage design methods.
