Unlocking the full potential of Knowledge Graphs (KGs) to enable or enhance various semantic and other applications requires Data Management Systems (DMSs) to efficiently store and process the content of KGs. However, the increases in the size and variety of KG datasets as well as the growing diversity of KG queries pose efficiency challenges for the current generation of DMSs to the extent that the performance of representative DMSs tends to vary significantly across diverse query types and no single platform dominates performance. We present our extensible prototype, SymphonyDB, as an approach to addressing this problem based on a polyglot model of query processing as part of a multi-database system supported by a unified access layer that can analyze/translate individual queries just-in-time and match each to the likely best-performing DMS among Virtuoso, Blazegraph, RDF-3X, and MongoDB as representative DMSs that are included in our prototype at this time. The results of our experiments with the prototype over well-known KG benchmark datasets and queries point to the efficiency and consistency of its performance across different query types and datasets.
翻译:释放知识图(KGs)的全部潜力,以促成或加强各种语义和其他应用,需要数据管理系统(DMSs)高效率地储存和处理KGs的内容。然而,KG数据集的规模和种类的扩大,以及KG查询的日益多样化,对目前生成的DMS带来了效率挑战,因为具有代表性的DMS的性能往往在不同查询类型之间有很大差异,没有单一平台主导性能。我们提出我们可扩展的原型(SymphonyDB),作为解决这一问题的一种办法,其基础是一个多数据库系统查询处理的多球模型,由统一的存取层支持,能够及时分析/翻译个别查询,并符合Virtuso、Blazegraph、RDF-3X和MongoDB等具有代表性的DMS之间可能的最佳DMS,这些都包含在我们的原型中。我们与众所周知的KG基准数据集原型的实验结果和查询显示其在不同类型和数据中的效率和一致性。