Epistemic Graph

Epistemic graphs represent knowledge and belief, extending traditional graph structures to model uncertainty and subjective perspectives. Current research focuses on developing algorithms to learn and reason with these graphs, including approaches leveraging techniques like query rewriting for controlled query evaluation and latent Dirichlet allocation for automated knowledge extraction from textual data. These models find applications in diverse fields, such as enhancing deep learning models by integrating structured knowledge for improved performance in tasks like few-shot learning and cross-domain recognition, and improving the efficiency of analyzing large datasets like those generated by online discussions. The ultimate goal is to create more robust and interpretable systems capable of handling incomplete or uncertain information.