Knowledge Embeddings

Knowledge embeddings represent knowledge graphs—structured collections of facts—as vectors in a continuous space, aiming to capture semantic relationships between entities and concepts. Current research focuses on improving embedding quality through techniques like multilingual support, task-specific adaptation (e.g., using LoRA), and incorporating both structural and textual information from ontologies and language models. These advancements are driving progress in diverse applications, including human-robot interaction, improved natural language processing tasks (like summarization and question answering), and enhanced medical image analysis. The ultimate goal is to create more robust and informative knowledge representations for a wide range of applications.