Data Quality Principles in the Semantic Web

TL;DR

This paper extends data quality principles to the Semantic Web, emphasizing their importance for ensuring data fit for use, improving decision-making, and enhancing long-term data integration and interoperability.

Contribution

It introduces five main classes of data quality principles tailored for the Semantic Web, based on extensive industrial experience.

Findings

Identification of five data quality classes for Semantic Web

Principles applicable at all data management stages

Enhanced decision-making and data interoperability

Abstract

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.