论文标题
知识图查询性能的比较分析
A Comparative Analysis of Knowledge Graph Query Performance
论文作者
论文摘要
随着知识图（KGS）继续获得广泛的动力以供不同的域使用，存储相关的kg内容并有效地执行查询变得越来越重要。已经采用了一系列数据管理系统（DMS）来处理KGS。本文旨在对各种DMS和KG查询类型的查询性能进行深入分析。我们的目的是针对主要的查询类型，即对四种主要的DMS类型（即行，列，图形，图形和文档商店）提供细粒度的比较分析，即主题对象，主题对象，类似树状和可选的连接。特别是，我们使用五个著名的基准测试基准，即BSBM，Watdiv，Fishmark，Bownabench和BioBench-Allie。我们的结果表明，没有单个DMS在四种查询类型中显示出卓越的查询性能。特别是，对于树状连接而言，行店和列储物的Virtuoso的倍数快3-8，BlazeGraph对于主题 - 对象加入的速度约为一个数量级，而MongoDB的高度选择性查询的速度更快。
As Knowledge Graphs (KGs) continue to gain widespread momentum for use in different domains, storing the relevant KG content and efficiently executing queries over them are becoming increasingly important. A range of Data Management Systems (DMSs) have been employed to process KGs. This paper aims to provide an in-depth analysis of query performance across diverse DMSs and KG query types. Our aim is to provide a fine-grained, comparative analysis of four major DMS types, namely, row-, column-, graph-, and document-stores, against major query types, namely, subject-subject, subject-object, tree-like, and optional joins. In particular, we analyzed the performance of row-store Virtuoso, column-store Virtuoso, Blazegraph (i.e., graph-store), and MongoDB (i.e., document-store) using five well-known benchmarks, namely, BSBM, WatDiv, FishMark, BowlognaBench, and BioBench-Allie. Our results show that no single DMS displays superior query performance across the four query types. In particular, row- and column-store Virtuoso are a factor of 3-8 faster for tree-like joins, Blazegraph performs around one order of magnitude faster for subject-object joins, and MongoDB performs over one order of magnitude faster for high-selective queries.
