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 …

Identifying temporal and subevent relationships between different events (ie, event relation
extraction) is an important step towards event-centric natural language processing, which …

Abstract Knowledge Graph (KG) stores human knowledge facts in an intuitive graphical
structure but faces challenges such as incomplete construction or inability to handle new …

Temporal reasoning is a crucial natural language processing (NLP) task, providing a
nuanced understanding of time-sensitive contexts within textual data. Although recent …

The task of predicting entities and relations in Temporal Knowledge Graph (TKG)
extrapolation is crucial and has been studied extensively. Mainstream algorithms, such as …

Predicting potential facts in the future, Temporal Knowledge Graph (TKG) extrapolation
remains challenging because of the deep dependence between the temporal association …

A Temporal Knowledge Graph (TKG) is a sequence of KGs with respective timestamps,
which adopts quadruples in the form of (\emph {subject},\emph {relation},\emph …

Predicting future events in dynamic knowledge graphs has attracted significant attention.
Existing work models the historical information in a holistic way, which achieves satisfactory …

Temporal knowledge graph (TKG) extrapolation aims to predict future unknown events
(facts) based on historical information, and has attracted considerable attention due to its …