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Pattern Recognition in Embedded Systems: An Overview Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Quality Metrics for Linked Open Data

Authors : Behshid Behkamal, Mohsen Kahani, Ebrahim Bagheri

Published in: Database and Expert Systems Applications

Publisher: Springer International Publishing

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Abstract

The vision of the Linked Open Data (LOD) initiative is to provide a model for publishing data and meaningfully interlinking such dispersed but related data. Despite the importance of data quality for the successful growth of the LOD, only limited attention has been focused on quality of data prior to their publication on the LOD. This paper focuses on the systematic assessment of the quality of datasets prior to publication on the LOD cloud. To this end, we identify important quality deficiencies that need to be avoided and/or resolved prior to the publication of a dataset. We then propose a set of metrics to measure and identify these quality deficiencies in a dataset. This way, we enable the assessment and identification of undesirable quality characteristics of a dataset through our proposed metrics.

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previous chapter Filtering Inaccurate Entity Co-references on the Linked Open Data
next chapter A Framework of Write Optimization on Read-Optimized Out-of-Core Column-Store Databases
Footnotes
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Metadata
Title
Quality Metrics for Linked Open Data
Authors
Behshid Behkamal
Mohsen Kahani
Ebrahim Bagheri
Copyright Year
2015
Book
Database and Expert Systems Applications

Print ISBN: 978-3-319-22848-8

Electronic ISBN: 978-3-319-22849-5

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-22849-5_11

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