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 …

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

Temporal knowledge graphs (TKGs) model the temporal evolution of events and have
recently attracted increasing attention. Since TKGs are intrinsically incomplete, it is …

The temporal knowledge graph builds upon the static knowledge graph by introducing the
time dimension and finds extensive applications in real artificial intelligence scenarios …

Temporal knowledge graph completion that predicts missing links for incomplete temporal
knowledge graphs (TKG) is gaining increasing attention. Most existing works have achieved …

Temporal knowledge graphs (TKGs) serve as powerful tools for storing and modeling
dynamic facts, holding immense potential in anticipating future facts. Since future facts are …

Temporal knowledge graphs (TKGs) are used for dynamically modeling facts in the temporal
dimension, and are widely used in various fields. However, existing reasoning models often …

Since the facts in the knowledge graph (KG) cannot be updated automatically over time,
some facts have temporal conflicts. To discover and eliminate the temporal conflicts in the …