Quality-driven information filtering using the WIQA policy framework

• Published in: Web semantics Vol. 7; no. 1; pp. 1 - 10
• Main Authors: Bizer, Christian, Cyganiak, Richard
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2009
• Subjects: Information quality; Quality-driven information filtering; Semantic Web; Semantic Web; Information quality; Quality-driven information filtering
• ISSN: 1570-8268; 1873-7749
• DOI: 10.1016/j.websem.2008.02.005

Web-based information systems, such as search engines, news portals, and community sites, provide access to information originating from numerous information providers. The quality of provided information varies as information providers have different levels of knowledge and different intentions. Users of web-based systems are therefore confronted with the increasingly difficult task of selecting high-quality information from the vast amount of web-accessible information. How can information systems support users to distinguish high-quality from low-quality information? Which filtering mechanisms can be used to suppress low-quality information? How can filtering decisions be explained to the user? This article identifies information quality problems that arise in the context of web-based systems, and gives an overview of quality indicators as well as information quality assessment metrics for web-based systems. Afterwards, we introduce the WIQA—Information Quality Assessment Framework. The framework enables information consumers to apply a wide range of policies to filter information. The framework employs the Named Graphs data model for the representation of information together with quality-related meta-information. The framework uses the WIQA-PL policy language for expressing information filtering policies against this data model. WIQA-PL policies are expressed in the form of graph patterns and filter conditions. This allows the compact representation of policies that rely on complex meta-information such as provenance chains or combinations of provenance information and background information about information providers. In order to facilitate the information consumers’ understanding of filtering decisions, the framework generates explanations of why information satisfies a specific policy.