A comprehensive quality model for Linked Data

نویسندگان

  • Filip Radulovic
  • Nandana Mihindukulasooriya
  • Raúl García-Castro
  • Asunción Gómez-Pérez
چکیده

With the increasing amount of Linked Data published on the Web, the community has recognised the importance of the quality of such data and a number of initiatives have been undertaken to specify and evaluate Linked Data quality. However, these initiatives are characterised by a high diversity in terms of the quality aspects that they address and measure. This leads to difficulties in comparing and benchmarking evaluation results, as well as in selecting the right data source according to certain quality needs. This paper presents a quality model for Linked Data, which provides a unique terminology and reference for Linked Data quality specification and evaluation. The mentioned quality model specifies a set of quality characteristics and quality measures related to Linked Data, together with formulas for the calculation of measures. Furthermore, this paper also presents an extension of the W3C Data Quality Vocabulary that can be used to capture quality information specific to Linked Data, a Linked Data representation of the Linked Data quality model, and a use case in which the benefits of the quality model proposed in this paper are presented in a tool for Linked Data evaluation.

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عنوان ژورنال:
  • Semantic Web

دوره 9  شماره 

صفحات  -

تاریخ انتشار 2018