Benchmarking Graph Database Backends - What Works Well with Wikidata?

T. Kovács, Gábor Simon, G. Mezei
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引用次数: 8

Abstract

Knowledge bases often utilize graphs as logical model. RDF-based knowledge bases (KB) are prime examples, as RDF (Resource Description Framework) does use graph as logical model. Graph databases are an emerging breed of NoSQL-type databases, offering graph as the logical model. Although there are specialized databases, the so-called triple stores, for storing RDF data, graph databases can also be promising candidates for storing knowledge. In this paper, we benchmark different graph database implementations loaded with Wikidata, a real-life, large-scale knowledge base. Graph databases come in all shapes and sizes, offer different APIs and graph models. Hence we used a measurement system, that can abstract away the API differences. For the modeling aspect, we made measurements with different graph encodings previously suggested in the literature, in order to observe the impact of the encoding aspect on the overall performance.  
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对图数据库后端进行基准测试-什么能很好地与维基数据一起工作?
知识库通常使用图作为逻辑模型。基于RDF的知识库(KB)就是最好的例子,因为RDF(资源描述框架)确实使用图作为逻辑模型。图数据库是nosql类型数据库的一个新兴品种,它提供图作为逻辑模型。虽然有专门的数据库,即所谓的三重存储,用于存储RDF数据,但图数据库也可能是存储知识的有希望的候选者。在本文中,我们对加载了Wikidata(一个真实的大规模知识库)的不同图数据库实现进行了基准测试。图数据库有各种形状和大小,提供不同的api和图模型。因此,我们使用了一个测量系统,它可以抽象出API的差异。在建模方面,我们使用文献中提出的不同的图编码进行测量,以观察编码方面对整体性能的影响。
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