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This paper defines and relates several important concepts in data fusion and natural language understanding: situation, relation, relationship and context. In data fusion - as in other problem-solving applications - contextual reasoning involves inferring desired information (ldquoproblem variablesrdquo) on the basis of other available information (ldquocontext variablesrdquo). Relevant contexts are often not self-evident, but must be discovered or selected as a means to problems-solving. Therefore, context exploitation involves an integration of data fusion with planning and control functions. These concepts can be generalized to apply in very diverse context exploitation applications, to include natural language understanding; which similarly involves data alignment, association and estimation of speaker/ authorspsila intended meanings and references. Discovering and selecting useful context variables is an abductive data fusion/ management problem that can be characterized in a utility/ uncertainty framework.