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 Knowledge graph completion is the task of inferring missing facts based on existing
data in a knowledge graph. Temporal knowledge graph completion (TKGC) is an extension …

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 Knowledge graphs have been demonstrated to be an effective tool for numerous
intelligent applications. However, a large amount of valuable knowledge still exists implicitly …

Entity alignment aims to identify equivalent entity pairs between different knowledge graphs
(KGs). Recently, the availability of temporal KGs (TKGs) that contain time information created …

Knowledge Graphs (KGs) are a collection of facts describing entities connected by
relationships. KG embeddings map entities and relations into a vector space while …

Knowledge graph (KG) alignment is to match entities in different KGs, which is important to
knowledge fusion and integration. Temporal KGs (TKGs) extend traditional Knowledge …

Temporal Knowledge Graph (TKG) reasoning often involves completing missing factual
elements along the timeline. Although existing methods can learn good embeddings for …