In examples, a schema augmentation system for exploratory research leverages intelligence from a machine learning model to augment such tasks by leveraging intelligence derived from machine learning capabilities. Augmenting tasks include schematization of content, such as information units and groupings of information units. Based on the schematization of such content, semantic proximities for information units are determined. The semantic proximities may be used to identify and present potentially relevant information units, for example to accelerate the exploratory research task at hand. As such, users engaged in consumption of heterogeneous content (e.g., across client applications and/or content sources), may receive machine-augmented support to find potential information units. To optimize machine training, user input may be received, such that the system may intelligently augment the user's exploratory research task based on the semantic coherence of the content processed from information units and associated user behavior.