Representation learning of medical Knowledge Graph (KG) is an important task and forms
the fundamental process for intelligent medical applications such as disease diagnosis and …

Complex node interactions are common in knowledge graphs, and these interactions also
contain rich knowledge information. However, traditional methods usually treat a triple as a …

Representation learning of knowledge graphs (KGs) aims to embed both entities and
relations as vectors in a continuous low-dimensional space, which has facilitated various …

Traditional knowledge graphs (KG) representation learning focuses on the link information
between entities, and the effectiveness of learning is influenced by the complexity of KGs …

Background: Knowledge discovery from treatment data records from Chinese physicians is a
dramatic challenge in the application of artificial intelligence (AI) models to the research of …

The purpose of knowledge representation learning is to learn the vector representation of
research objects projected by a matrix in low-dimensional vector space and explore the …

Knowledge graph, or knowledge base, plays an important role in a variety of applications in
the field of artificial intelligence. In both research and application of knowledge graph …

Entity representation learning is both fundamental and crucial for link prediction in
knowledge graph. Existing entity representation learning approaches mainly focus on …

Representation learning of knowledge graphs encodes both entities and relations into a
continuous low-dimensional space. Most existing methods focus on learning …

Much of biomedical and healthcare data is encoded in discrete, symbolic form such as text
and medical codes. There is a wealth of expert-curated biomedical domain knowledge …