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Over the last years, the Semantic Web has been growing steadily. Today, we count more than 10,000 datasets made available online following Semantic Web standards. Nevertheless, many applications, such as data integration, search, and interlinking, may not take the full advantage of the data without having a priori statistical information about its internal structure and coverage. In fact, there are already a number of tools, which offer such statistics, providing basic information about RDF datasets and vocabularies. However, those usually show severe deficiencies in terms of performance once the dataset size grows beyond the capabilities of a single machine. In this paper, we introduce a software component for statistical calculations of large RDF datasets, which scales out to clusters of machines. More specifically, we describe the first distributed inmemory approach for computing 32 different statistical criteria for RDF datasets using Apache Spark. The preliminary results show that our distributed approach improves upon a previous centralized approach we compare against and provides approximately linear horizontal scale-up. The criteria are extensible beyond the 32 default criteria, is integrated into the larger SANSA framework and employed in at least four major usage scenarios beyond the SANSA community.

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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The size and number of knowledge graphs have increased tremendously in recent years. In the meantime, the distributed data processing technologies have also advanced to deal with big data and large scale knowledge graphs. This chapter introduces Scalable Semantic Analytics Stack (SANSA), that addresses the challenge of dealing with large scale RDF data and provides a unified framework for applications like link prediction, knowledge base completion, querying, and reasoning. We discuss the motivation, background and the architecture of SANSA. SANSA is built using general-purpose processing engines Apache Spark and Apache Flink. After reading this chapter, the reader should have an understanding of the different layers and corresponding APIs available to handle Knowledge Graphs at scale using SANSA.