The function of the mid level processing is to act as a transition layer between the low level processing which is more data oriented and the high level reasoning which is related more closely to an abstract model. Clustering or classification activities are examples of functionality that typically occur in the middle layer of the analysis model. These operations relate to the second boundary in the process stack, between "inferred entities" and "relationships". For example, once a set of features has been detected by the bottom layer, mid level components could be employed to identify relationships between them and other information present in the knowledgebase. The resulting relationships are also stored back in the knowledgebase for further consideration by other parts of the framework, either at this level or above.
Uncertainty often manifests itself in the mid level of the model as vagueness, ambiguity, or incoherence.
