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 …

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 …

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

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 …

Abstract Temporal Knowledge Graph Reasoning (TKGR) is the process of utilizing temporal
information to capture complex relations within a Temporal Knowledge Graph (TKG) to infer …

A Temporal Knowledge Graph (TKG) is a sequence of KGs corresponding to different
timestamps. TKG reasoning aims to predict potential facts in the future given the historical …

Temporal Knowledge Graphs (TKGs) have been developed and used in many different
areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great …

In this paper, we investigate a realistic but underexplored problem, called few-shot temporal
knowledge graph reasoning, that aims to predict future facts for newly emerging entities …