Modeling and Visualizing Human Experience in a Knowledge Graph: Understanding and Predicting Emotional Reactions to Life Events

This paper introduces an innovative approach to modeling and visualizing human emotional responses to life events using a knowledge graph. We leverage federated learning to predict and analyze emotional reactions across distributed datasets while ensuring privacy and security compliance. By integrating federated learning with a knowledge graph framework, we demonstrate how decentralized data sources, including social media, healthcare, and IoT data, can be collaboratively used to model human experiences at scale. The use of federated learning facilitates privacy-preserving predictions of emotional reactions, particularly in sensitive areas such as mental health and personalized recommendations, thereby directly contributing to a deeper understanding of search engines.