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

Temporal characteristics are prominently evident in a substantial volume of knowledge,
which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia …

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 …

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 …

Abstract Temporal Knowledge Graph (TKG) reasoning aims to predict future facts based on
historical data. However, due to the limitations in construction tools and data sources, many …

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

Knowledge graph completion (KGC) can predict missing links and is crucial for real-world
knowledge graphs, which widely suffer from incompleteness. KGC methods assume a …

Temporal knowledge graph, serving as an effective way to store and model dynamic
relations, shows promising prospects in event forecasting. However, most temporal …

Temporal graphs are widely used to model dynamic systems with time-varying interactions.
In real-world scenarios, the underlying mechanisms of generating future interactions in …