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The Web offers autonomous and frequently useful resources in growing manner. User Generated Content (UGC) like Wikis, Weblogs or Webfeeds often do not have one responsible authorship or declared experts who checked the created content for e.g. accuracy, availability, objectivity or reputation. The user is not able easily, to control the quality of the content he receives. If we want to utilize the distributed information flood as a linked knowledge base for higher-layered applications - e.g. for knowledge transfer and learning - information quality (iq) is a very important and complex aspect to analyze, personalize and annotate resources. In general, low information quality is one of the main discriminators of data sources on the Web. Assessing information quality with measurable terms can offer a personalized and smart view on a broad, global knowledge base. We developed the qKAI application framework to utilize available, distributed data sets in a practically manner. In the following we present our adaption of information quality aspects to qualify Web resources based on a three-level assessment model. We deploy knowledge-related iq-criteria as tool to implement iq-mechanisms stepwise into the qKAI framework. Here, we exemplify selected criteria of information quality in qKAI like relevance or accuracy. We derived assessment methods for certain iq-criteria enabling rich, game-based user interaction and semantic resource annotation. Open Content is embedded into knowledge games to increase the users' access and learning motivation. As side effect the resources' quality is enhanced stepwise by ongoing user interaction.