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A significant obstacle for service robots is the execution of complex tasks in real environments. For example, it is not easy for service robots to find objects that are partially observable and are located at a place which is not identical but near the place where the robots saw them previously. To overcome the challenge effectively, robot knowledge represented as a semantic network can be extremely useful. This paper presents an ontology-based unified robot knowledge framework that integrates low-level data with high-level knowledge for robot intelligence. This framework consists of two sections: knowledge description and knowledge association. Knowledge description includes comprehensively integrated robot knowledge derived from low-level knowledge regarding perceptual features, part objects, metric maps, and primitive behaviors, as well as high-level knowledge about perceptual concepts, objects, semantic maps, tasks, and contexts. Knowledge association uses logical inference with both unidirectional and bidirectional rules. This characteristic enables reasoning to be performed even when only a partial information is available. The experimental results that demonstrate the advantages of using the proposed knowledge framework are also presented.