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 …

With the urgent demand for generalized deep models, many pre-trained big models are
proposed, such as bidirectional encoder representations (BERT), vision transformer (ViT) …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

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 …

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

Mining valuable hidden knowledge from large-scale data relies on the support of reasoning
technology. Knowledge graphs, as a new type of knowledge representation, have gained …

Knowledge graphs are important resources for many artificial intelligence tasks but often
suffer from incompleteness. In this work, we propose to use pre-trained language models for …

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 …

Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

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