一种知识图谱的双存储结构
A Dual-Store Structure for Knowledge Graphs
摘要：
为了有效地管理各个领域中日益增多的知识图谱，知识图谱存储管理成为一个研究热点。现有的方法被分类为关系存储和原生图存储。关系存储能够存储大规模的知识图谱，便于知识更新，但当知识图谱查询的选择性较大时，查询性能明显下降。原生图存储由于其无索引的邻接属性而在处理复杂的知识图谱查询时是有效的，但是由于有限的存储预算或不灵活的更新过程，它们不适用于管理大规模的知识图谱。基于此，我们提出了一种双存储结构，它利用图存储来加速关系存储中的复杂查询过程。然而，确定在什么时间将什么数据从关系存储传输到图存储是具有挑战性的。为了解决这个问题，我们制定了它作为一个马尔可夫决策过程，并得出一个物理设计调谐器DOTIL的基础上强化学习。使用DOTIL，双存储结构可以适应动态变化的工作负载。在真实知识图谱上的实验结果表明，与最常用的关系存储相比，我们提出的双存储结构平均提高查询性能达43.72%。
英文摘要：
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.
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