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Varied context reasoning approaches are used across a variety of applications in the pervasive computing domain including: health monitoring, intrusion detection, airport security and military target tracking. The same types of context are being inferred in diverse ways across a number of platforms. For example, human activity has been inferred using a number of statistical, ontological, and logical approaches. What we do not find in sufficient measure is context aware reasoning framework and knowledge reuse. Lack of reuse limits rapid context aware application development. This further restrains innovation. Among the factors contributing to this situation are: i) insufficient modeling formalisms; ii) limited generalized APIs for reasoning; and iii) inefficient and inflexible reasoning choices that fail to meet application needs. Utilizing reusable components for application development is a fundamental challenge in generalizing context reasoning. In this paper, we attempt to design and develop a foundation for implementing a generalized hierarchical hybrid context reasoning engine for pervasive applications. With such a framework we can achieve greater context reasoning framework and knowledge reuse, support for context modularization and improved context aware application performance.