Knowledge Graph Representation Learning

Knowledge graph representation learning (KGRL) aims to encode the entities and relationships within knowledge graphs into low-dimensional vector spaces, facilitating various downstream tasks like link prediction and entity alignment. Current research emphasizes improving model robustness against adversarial attacks and biases, exploring diverse architectures like graph neural networks and transformers, and developing more effective negative sampling techniques for training. These advancements are crucial for enhancing the accuracy and reliability of knowledge graph applications across diverse fields, including healthcare, finance, and education, where reliable knowledge inference is paramount.