Knowledge Graph Embedding

Knowledge Graph Embedding is a technique that maps entities and relations in a knowledge graph to a low-dimensional vector space, aiming to capture their intrinsic semantic and structural information through learning the representations of entities and relations. The goal of this technology is to enhance the reasoning and expressive capabilities of knowledge graphs, enabling them to perform better in tasks such as link prediction, entity classification, and relation extraction. The application value of Knowledge Graph Embedding lies in its ability to effectively support various scenarios including intelligent question answering, recommendation systems, and natural language processing, thereby improving the intelligence level and user experience of these systems.