PROBABILISTIC GRAPHICAL NETWORKS

Provided are processes of balancing between exploration and optimization with knowledge discovery processes applied to unstructured data with tight interrogation budgets. In an evaluation for which features are structured (e.g., either in a structured evaluation or determined from unstructured data and provided for evaluation) a probabilistic graphical network may graph inputs of machine learning model(s) and outputs of the machine learning model(s) as graphical elements, where one or more edges or nodes, or values associated therewith, may be based on the outputs. For example, as a set of ranking entities engage an expert system during an evaluation, the expert system may determine and update a probabilistic graphical network that represents a state of the evaluation (e.g., at a point in time after one or more ranking events), or (e.g., after completion) a final state and determined scores based the inputs provided by the ranking entities.