  Human psychology plays an important role in organizational performance.
However, understanding our employees is a difficult task due to issues such as
psychological complexities, unpredictable dynamics, and the lack of data.
Leveraging evidence-based psychology knowledge, this paper proposes a hybrid
machine learning plus ontology-based reasoning system for detecting human
psychological artifacts at scale. This unique architecture provides a balance
between system's processing speed and explain-ability. System outputs can be
further consumed by graph science and/or model management system for optimizing
business processes, understanding team dynamics, predicting insider threats,
managing talents, and beyond.
