With the explosive growth of artificial intelligence (AI) and big data, it has become vitally
important to organize and represent the enormous volume of knowledge appropriately. As …

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

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

Knowledge graph completion (KGC) is a hot topic in knowledge graph construction and
related applications, which aims to complete the structure of knowledge graph by predicting …

Knowledge graph embedding (KGE) models represent each entity and relation of a
knowledge graph (KG) with low-dimensional embedding vectors. These methods have …

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

One of the fundamental problems in Artificial Intelligence is to perform complex multi-hop
logical reasoning over the facts captured by a knowledge graph (KG). This problem is …

A knowledge graph (KG), also known as a knowledge base, is a particular kind of network
structure in which the node indicates entity and the edge represent relation. However, with …

In this paper, we study the problem of learning representations of entities and relations in the
knowledge graph for the link prediction task. Our idea is based on the observation that the …

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