Structural Knowledge

Structural knowledge, encompassing the inherent relationships and patterns within data, is a burgeoning research area aiming to improve machine learning model performance by explicitly incorporating structural information alongside traditional features. Current research focuses on integrating structural knowledge into various model architectures, including graph neural networks and pre-trained language models, often employing techniques like structure-aware attention mechanisms and knowledge graph embeddings to enhance representation learning. This work holds significant implications for diverse fields, improving the accuracy and efficiency of tasks such as knowledge graph completion, protein function prediction, and question answering by leveraging the rich information embedded in data structures.