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Systems with human-level intelligence must both be flexible and be able to reason in an appropriate time scale. These two goals are in tension, as manifest by the contrasting properties of structured knowledge-based systems (e.g., involving scripts and frames) and general inference algorithms. The problem of resolving ambiguous, implicit and non-literal references exemplifies many of these difficulties. We describe an approach, called reasoned unification, for dealing with these challenges by representing and jointly reasoning over linguistic and non-linguistic knowledge (including structures such as scripts and frames) within the same inference framework. Reasoned unification enables a treatment of several reference resolution phenomena that to our knowledge have not previously been the subject of a unified analysis. This analysis illustrates how reasoned unification can resolve many difficult problems with using complex knowledge structures while maintaining their benefits.