LD Sniffer: A Quality Assessment Tool for Measuring the Accessibility of Linked Data

Mihindukulasooriya, Nandana; García‐Castro, Raúl; Gómez-Pérez, Asuncíon

doi:10.1007/978-3-319-58694-6_20

Cited by 4 publications

(2 citation statements)

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“…In addition, we test the performance of our approach on large-scale RDF datasets while their approach is not experimentally evaluated. LD-Sniffer [17], is a tool for assessing the accessibility of Linked Data resources according to the metrics defined in the Linked Data Quality Model. The limitation of this tool, besides that it is a centralized version, is that it does not provide most of the quality assessment metrics defined in [22].…”

Section: Related Workmentioning

confidence: 99%

“…To date, a limited number of solutions have been conceived to offer quality assessment of RDF datasets [11,13,4,10]. But, these methods can either be used on a small portion of large datasets [13] or narrow down to specific problems e.g., syntactic accuracy of literal values [4], or accessibility of resources [17]. In general, these existing efforts show severe deficiencies in terms of performance when data grows beyond the capabilities of a single machine.…”

Section: Introductionmentioning

confidence: 99%

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A Scalable Framework for Quality Assessment of RDF Datasets

Sejdiu

Rula

Lehmann

et al. 2019

Lecture Notes in Computer Science

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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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Section: Related Workmentioning

confidence: 99%