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 …

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 …

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 …

In this paper, we study the problem of learning probabilistic logical rules for inductive and
interpretable link prediction. Despite the importance of inductive link prediction, most …

Despite the importance and abundance of temporal knowledge graphs, most of the current
research has been focused on reasoning on static graphs. In this paper, we study the …

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 …

Abstract Knowledge Graph Completion (KGC) is a fundamental problem for temporal
knowledge graphs (TKGs), and TKGs embedding methods are one of the essential methods …

It is natural and effective to use rules for representing explicit knowledge in knowledge
graphs. However, it is challenging to learn rules automatically from very large knowledge …

Traditional knowledge graph completion mainly focuses on static knowledge graph.
Although there are efforts studying temporal knowledge graph completion, they assume that …

Many recent network embedding algorithms use negative sampling (NS) to approximate a
variant of the computationally expensive Skip-Gram neural network architecture (SGA) …