Abstract: Systems, devices, computer-implemented methods, and/or computer program products that can facilitate event extraction from heterogeneous data sources using an active learning framework are provided. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise a training component and a learning-based sample component. The training component can train one or more models to recognize one or more events from data input into the system, extract one or more triggers from the data input into the system, extract one or more arguments from the data input into the system, or extract one or more roles from the data input into the system. The learning-based sample component can determine a sampled dataset by applying a learning-based sample to the one or more models.

Abstract: Systems, devices, computer-implemented methods, and/or computer program products that facilitate artificial intelligence (AI)-assisted curation of non-pharmaceutical intervention (NPI) data from heterogeneous data sources. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise an extraction component and a change detection component. The extraction component can extract candidate non-pharmaceutical intervention (NPI) events from data associated with a defined disease. The change detection component can evaluate the candidate NPI events for inclusion in a dataset storing NPI events in a defined format.