Data quality principles in the semantic web

Assaf, Ahmad; Senart, Aline

ICSC 2012, 6th IEEE International Conference on Semantic Computing, September 19-21, 2012, Palermo, Italy / Also on ArXiv

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, we propose to extend the data quality principles to the context of Semantic Web. Based on our extensive industrial experience in data integration, we identify five main classes suited for data quality in Semantic Web. For each class, we list the principles that are involved at all stages of the data management process. Following these principles will provide a sound basis for better decision-making within organizations and will maximize long-term data integration and interoperability.

Detail

Document

ARXIV

DOI

BIBTEX

Type:

Conference

City:

Palermo

Date:

2012-09-19

Department:

Data Science

Eurecom Ref:

4448

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