Knowledge Graph Embeddings

Knowledge graph embeddings represent the nodes and relationships within knowledge graphs as low-dimensional vectors, aiming to capture semantic relationships and facilitate tasks like link prediction and knowledge graph completion. Current research emphasizes improving model accuracy and addressing challenges such as predictive multiplicity (conflicting predictions from different embeddings) and interpretability, with ongoing work exploring various embedding models (e.g., TransE, ComplEx, RotatE) and their integration with graph neural networks and logical rules. This field is significant for its potential to enhance various applications, including biomedical research (e.g., drug discovery, clinical trial design), natural language processing, and cybersecurity, by enabling more efficient and accurate knowledge reasoning and inference.