Heterogeneous Knowledge

Heterogeneous knowledge research focuses on integrating and reasoning across diverse data sources—including text, knowledge graphs, and databases—to improve the performance of various tasks. Current research emphasizes developing novel model architectures, such as graph neural networks and large language models, often incorporating techniques like message passing and prompt learning to effectively fuse these disparate knowledge representations. This work is significant because it addresses limitations of single-source approaches, leading to improved accuracy and explainability in applications like question answering, recommendation systems, and cognitive diagnosis. The resulting advancements have broad implications for fields requiring sophisticated information processing and knowledge integration.