KG Embeddings

Knowledge graph embeddings (KGEs) aim to represent entities and relationships within knowledge graphs as numerical vectors, enabling machine learning models to perform tasks like link prediction and question answering. Current research focuses on improving the accuracy and interpretability of these embeddings, addressing issues like predictive multiplicity (conflicting predictions from different models) and developing methods to incorporate diverse data sources, including text and multiple modalities, using techniques such as transformer-based models and graph neural networks. The resulting advancements have significant implications for various fields, improving the efficiency and accuracy of knowledge-based systems in applications ranging from drug discovery and international trade analysis to question answering and image editing.