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

随着互联网技术和应用模式的迅猛发展, 表达方式丰富直观的知识图谱得到了大量关注,
在知识表示学习方面积累了丰富研究成果, 这些研究已在垂直搜索, 智能问答等应用领域发挥了 …

Most existing knowledge graphs suffer from incompleteness, which can be alleviated by
inferring missing links based on known facts. One popular way to accomplish this is to …

This paper studies learning logic rules for reasoning on knowledge graphs. Logic rules
provide interpretable explanations when used for prediction as well as being able to …

Recently, knowledge graph embeddings (KGEs) have received significant attention, and
several software libraries have been developed for training and evaluation. While each of …

Knowledge graph embedding (KGE) models learn algebraic representations of the entities
and relations in a knowledge graph. A vast number of KGE techniques for multi-relational …

Human-curated knowledge graphs provide critical supportive information to various natural
language processing tasks, but these graphs are usually incomplete, urging auto …

Link prediction attempts to predict whether an unseen edge exists based on only a portion of
the graph. A flurry of methods has been created in recent years that attempt to make use of …

Reasoning on the knowledge graph (KG) aims to infer new facts from existing ones.
Methods based on the relational path have shown strong, interpretable, and transferable …

Graph convolutional networks (GCNs)—which are effective in modeling graph structures—
have been increasingly popular in knowledge graph completion (KGC). GCN-based KGC …