Abstract
Whereas in the past, research and industry mainly focused on technological aspects related to the exposure of data and its
processing from remote sources, the concern in Semantic Web Data Management started to shift over to other non-technical
challenges when dealing with so called Linked Enterprise Data. Information Quality is such an aspect that plays an
important role in the process of selecting the best available data source in the Web, consolidating it with already existing
information and thereby improving the business value of the own data stock. Throughout the last decades, research has
already comprehensively dealt with the question of what quality is and how it can be interpreted through different
approaches. Surprisingly, it is all the more astounding that there are only vague and no concise and formal definitions of
the quality concept so far, especially in the Semantic Web context. A formalization would help to make quality calculations
more comparable and implementable. The paper addresses this challenge and raises the question on how to compute the
quality of a particular data source by combining different aspects from existing approaches resulting in a more concise
model. As a finding, data quality is expressed as the percentage to which degree a particular data source fulfills a set of
specified requirements in a certain context. The formalized definition of data quality helps to discuss specific assessment
aspects, and is exemplarily applied to a scenario from the CRM application domain
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