Data Quality Principles in the Semantic Web

The increasing size and availability of web data make data quality a core challenge in many applications. Principles of data quality are recognized as essential to ensure that data fit for their intended use in operations, decision-making, and planning. However, with the rise of the Semantic Web, new data quality issues appear and require deeper consideration. In this paper, the authors propose to extend the data quality principles to the context of Semantic Web. Based on their extensive industrial experience in data integration, they identify five main classes suited for data quality in Semantic Web. For each class, they list the principles that are involved at all stages of the data management process.

Provided by: Cornell University Topic: Data Management Date Added: Jul 2012 Format: PDF

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