论文标题
基于增强学习的有效RDF图表存储
Efficient RDF Graph Storage based on Reinforcement Learning
论文作者
论文摘要
知识图是人工智能的重要基石。大型知识图在各个领域的构建和释放对知识图数据管理提出了新的挑战。由于成熟度和稳定性，关系数据库也适用于RDF数据存储。但是，RDF图的复杂结构给关系数据库中RDF图的存储结构设计带来了挑战。为了解决困难的问题，本文采用加固学习（RL），以基于关系数据库优化RDF图的存储分区方法。我们将图形存储转换为马尔可夫决策过程，并开发用于图形存储设计的增强学习算法。对于有效的基于RL的存储设计，我们提出了RDF表的数据功能提取方法以及模型培训期间的查询重写优先策略。广泛的实验结果表明，我们的方法的表现优于现有的RDF存储设计方法。
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.
