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 …

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 …

Knowledge Graph (KG) reasoning that predicts missing facts for incomplete KGs has been
widely explored. However, reasoning over Temporal KG (TKG) that predicts facts in the …

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

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

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

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 …

Dynamic networks are used in a wide range of fields, including social network analysis,
recommender systems and epidemiology. Representing complex networks as structures …