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

In this article, we provide a comprehensive introduction to knowledge graphs, which have
recently garnered significant attention from both industry and academia in scenarios that …

Knowledge graph (KG) embeddings learn low-dimensional representations of entities and
relations to predict missing facts. KGs often exhibit hierarchical and logical patterns which …

There has been a surge of recent interest in graph representation learning (GRL). GRL
methods have generally fallen into three main categories, based on the availability of …

Abstract Query embedding (QE)---which aims to embed entities and first-order logical (FOL)
queries in low-dimensional spaces---has shown great power in multi-hop reasoning over …

Recently, hyperbolic deep neural networks (HDNNs) have been gaining momentum as the
deep representations in the hyperbolic space provide high fidelity embeddings with few …

Although self-/un-supervised methods have led to rapid progress in visual representation
learning, these methods generally treat objects and scenes using the same lens. In this …

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …

Abstract Knowledge graph (KG) embeddings have shown great power in learning
representations of entities and relations for link prediction tasks. Previous work usually …

Knowledge graphs (KGs) have gradually become valuable assets for many AI applications.
In a KG, a node denotes an entity, and an edge (or link) denotes a relationship between the …