ANSWER: Automatic Index Selector for Knowledge Graphs

Efficient access to knowledge graphs is identified as the basic premise to make full use of knowledge graphs. Since the query processing efficiency is mainly affected by index configuration, it is necessary to create effective indexes for knowledge graphs. However, none of existing studies of index selection focuses on the characteristics of knowledge graphs. To fill this gap, we propose an automatic index selector for knowledge graphs based on reinforcement learning, named ANSWER, to select an appropriate index configuration according to the historical workloads. However, it is challenging a learn a well-trained index selection model due to the large action space of reinforcement learning model and the requirement of lightweight embedding strategies. To address this problem, we first develop a novel predicate filter, which not only determines which vertical partitioning tables are valuable to create indexes, but also reduces the action space of model. Based on the filtered predicates, we derive an effective and lightweight encoder to not only embed the main features of workloads into the model, but also guarantee the high-efficiency of ANSWER. Experimental results on real-world knowledge graphs demonstrate the effectiveness of ANSWER in terms of knowledge graph query processing.