Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

Abstract Knowledge Graph (KG) provides high-quality structured knowledge for various
downstream knowledge-aware tasks (such as recommendation and intelligent question …

Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as
in information retrieval, natural language processing, recommendation systems, etc …

The knowledge graph (KG) that represents structural relations among entities has become
an increasingly important research field for knowledge-driven artificial intelligence. In this …

Many mathematical models have been leveraged to design embeddings for representing
Knowledge Graph (KG) entities and relations for link prediction and many downstream tasks …

Abstract Knowledge graph reasoning is the fundamental component to support machine
learning applications such as information extraction, information retrieval, and …

Abstract Graph Attention Networks (GATs) have proven a promising model that takes
advantage of localized attention mechanism to perform knowledge representation learning …

Identifying the potential associations among drugs, microbes, and diseases is of great
significance in exploring the pathogenesis and improving precision medicine. There are …

Translation-based knowledge graph embedding has been one of the most important
branches for knowledge representation learning since TransE came out. Although many …