一个可扩展的RDF数据集质量评估框架
A Scalable Framework for Quality Assessment of RDF Datasets
摘要：
在过去的几年里，关联数据不断增长。今天，我们统计了超过10，000个遵循关联数据标准的在线数据集。这些标准允许数据是机器可读的和可互操作的。然而，许多应用程序，如数据集成，搜索和互连，不能充分利用关联数据，如果它是低质量的。存在一些用于关联数据的质量评估的方法，但是它们的性能随着数据大小的增加而降低，并且快速增长超出了单个机器的能力。在本文中，我们提出DistQualityAssessment -一个开源的大型RDF数据集的质量评估的实现，可以扩展到一个机器集群。这是第一个使用Apache Spark为大型RDF数据集计算不同质量指标的分布式内存方法。我们还提供了一个质量评估模式，可用于生成可应用于大数据的新的可扩展指标。本文介绍的工作与SANSA框架集成，并已应用于SANSA社区以外的至少三个用例。结果表明，我们的方法是更通用的，高效的，可扩展性相比，以前提出的方法。
英文摘要：
Over the last years, Linked Data has grown continuously. Today, we count more than 10,000 datasets being available online following Linked Data standards. These standards allow data to be machine readable and inter-operable. Nevertheless, many applications, such as data integration, search, and interlinking, cannot take full advantage of Linked Data if it is of low quality. There exist a few approaches for the quality assessment of Linked Data, but their performance degrades with the increase in data size and quickly grows beyond the capabilities of a single machine. In this paper, we present DistQualityAssessment -- an open source implementation of quality assessment of large RDF datasets that can scale out to a cluster of machines. This is the first distributed, in-memory approach for computing different quality metrics for large RDF datasets using Apache Spark. We also provide a quality assessment pattern that can be used to generate new scalable metrics that can be applied to big data. The work presented here is integrated with the SANSA framework and has been applied to at least three use cases beyond the SANSA community. The results show that our approach is more generic, efficient, and scalable as compared to previously proposed approaches.
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