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This paper presents an approach to creating flexible general-logic representations from language for use in high-level reasoning tasks in cognitive modeling.  These representations are grounded in a large-scale ontology and emphasize the need for semantic breadth at the cost of syntactic breadth.  The task-independent interpretation process allows task-specific pragmatics to guide the interpretation process. In the context of a particular cognitive model, we discuss our use of limited abduction for interpretation and show results of its performance.