Data Quality Principles in the Semantic Web
This work addresses data quality issues for organizations using Semantic Web technologies, but it is incremental as it builds on established principles.
The paper tackles the challenge of data quality in the Semantic Web by extending existing principles to this context, identifying five main classes and listing principles for all stages of data management to improve decision-making and interoperability.
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.