In this paper, two approaches for computing the topological information content of function models in mechanical engineering design are developed and compared. Previously a metric for computing information content of functions and flows within function models was proposed. Here this metric is adapted to compute the information contained in the resulting connections of flows between functions in a function model. The first approach is based on uniform unconditional probability of a flow connecting any two functions within the model. The second approach is based on additional knowledge that the functions and flows in a model have limited compatibility, thereby reducing the choices for origin and destination functions for each flow. This additional knowledge is represented using a new graphical representation supported by syntactical grammar rules. Both approaches are then applied to an example function model. Comparison between the approaches shows that the inclusion of compatibility knowledge increases the expressiveness of function representations and reduces the uncertainty of function models.

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