Robotically manipulating objects can be very challenging when not all of the environment can be fully observed, e.g. in environments which are physically and visually accessible from only a single side. By using multimodal sensory feedback and symbolic reasoning, conclusions can be drawn about the presence of objects that cannot be observed directly. This paper presents the Symbolic Reasoning for Partially Observable Environments (SyRePOE) system, which uses an ontology to maintain its world model and a reasoner to infer information about unobservable objects. SyRePOE is demonstrated in simulation and on a real robot, where it is tasked with stocking a retail shelf.