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

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

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 …

Reasoning on temporal knowledge graphs (TKGR), aiming to infer missing events along the
timeline, has been widely studied to alleviate incompleteness issues in TKG, which is …

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

Large knowledge graphs often grow to store temporal facts that model the dynamic relations
or interactions of entities along the timeline. Since such temporal knowledge graphs often …

Temporal knowledge graph (TKG) reasoning is a crucial task that has gained increasing
research interest in recent years. Most existing methods focus on reasoning at past …

Knowledge graph reasoning is a critical task in natural language processing. The task
becomes more challenging on temporal knowledge graphs, where each fact is associated …

Conventional static knowledge graphs model entities in relational data as nodes, connected
by edges of specific relation types. However, information and knowledge evolve …