2020/01/05 by Tam N. Nguyen, Nguyen, Tam N.
Computer Science · Health Professions · #Cartography #Community Health and Development #Computers and Society (cs.CY) #FOS: Computer and information sciences #Geography #H.1 #I.7 #J.4 #Scale (ratio) #Social and Information Networks (cs.SI) #cs.CY #cs.SI
paper · pdf · doi:10.48550/arxiv.2001.09743
published in arXiv (Cornell University) (Cornell University) · 29 pages APA style (8 reference pages), 5 figures, 1 table
arxiv created 2020/01/05 · openalex publication_date 2020/01/05 · arxiv updated 2020/01/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.