links for 2011-04-07

  • The Data Quality Constraints Library provides generic SPARQL query templates that may be used to identify different types of data quality problems. The query templates are designed based on the SPARQL Inferencing framework (SPIN) [1]. The query templates can easily be adjusted to any Semantic Web data set and are immediately available to detect data quality problems in the data. The constraints library may also be used for relational data in conjunction with wrapping technologies such as D2RQ.

    The Data Quality Constraints Library is of interest for data publishers and data consumers. Data publishers may use the constraints to assure the published data is of high quality. Data consumers may use the constraints to check the quality state of the data before relying on it. The broad application of data quality constraints may, moreover, help to achieve trust in Semantic Web data.

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