WIQA

WIQA

The WIQA - Information Quality Assessment Framework is a set of software components for filtering information from the Web using a wide range of different filtering policies.

The framework has been designed to fulfill the following requirements:

  • Flexible Representation of Information together with Quality-Related Meta-information. Information quality assessment may rely on a wide range of different quality indicators. Which quality indicators are relevant depends on the application domain and the quality dimensions to be assessed. Important quality indicators in the context of web-based information systems are provenance information, ratings, and background information about information providers. The WIQA framework uses Named Graphs [CaBiHaSt05] as a flexible data model for representing information together with quality related meta-information.
  • Support for different Information Filtering Policies. The relevancy of different quality dimensions and the metrics used to assess these dimensions depend on the application domain, the quality indicators available, the task at hand and the subjective preferences of the information consumer. Therefore, information consumers use a wide range of different information filtering policies in different situations. The WIQA framework allows different policies to be employed for filtering information. Policies are expressed using a declarative policy language and can combine context-, content- and rating-based assessment metrics.
  • Explaining Filtering Decisions. The accuracy of assessment results is often uncertain due to the limited availability of quality indicators and the uncertain quality of the quality indicators themself. Therefore, the final subjective decision of an information consumer whether to trust or distrust assessment results depends on his understanding of the quality indicators and the assessment metrics that have been used in the assessment process. In order to support information consumers in their trust decision, the WIQA framework can generate detailed explanations about filtering decisions.

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