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For solving recognition tasks in order to navigate in unknown environments and to manipulate objects, humans seem to use at least the following crucial capabilities: abstraction (for storing higher-level concepts of things), common sense knowledge and prediction. Whereas the first and second provide the basis for situated recognition, the second and third serve for pruning the search space as it helps anticipating what (in an abstract sense) they will see next and where. The main goal of our current research is, how we could use such a kind of "common sense world knowledge" for guiding visual perception and understanding scenes. Therefore, we are combining an owl-ontology with the output of vision tools. The additional use of abstraction techniques tries to establish the possibility of detecting higher level concepts, such as arches composed of a variable number of parts. The goal is to finally find concepts such as doors and tables in arbitrary scenes in order to arrive at a generic recognition tool for home robots. The ontology should additionally provide task-specific information about the things to detect.