Inductive knowledge graph completion has been considered as the task of predicting
missing triplets between new entities that are not observed during training. While most …

Abstract Knowledge graphs represent human knowledge using triplets composed of entities
and relations. While most existing knowledge graph embedding methods only consider the …

Fraud detection aims to discover fraudsters deceiving other users by, for example, leaving
fake reviews or making abnormal transactions. Graph-based fraud detection methods …

Stemming from traditional knowledge graphs (KGs), hyper-relational KGs (HKGs) provide
additional key-value pairs (ie, qualifiers) for each KG fact that help to better restrict the fact …

Beyond-triple fact representations including hyper-relational facts with auxiliary key-value
pairs, temporal facts with additional timestamps, and nested facts implying relationships …

While a number of knowledge graph representation learning (KGRL) methods have been
proposed over the past decade, very few theoretical analyses have been conducted on …

Few-shot Knowledge Graph Completion (FKGC) has emerged as a significant area of
interest for addressing the long-tail problem in knowledge graphs. Traditional approaches …

By representing knowledge in a primary triple associated with additional attribute-value
qualifiers, hyper-relational knowledge graph (HKG) that generalizes triple-based knowledge …

Complex logical reasoning over knowledge graphs lies at the heart of many semantic
downstream applications and thus has been extensively explored in recent years. However …

Abstract Knowledge Graph (KG) embeddings are essential for link prediction over KGs.
Compared to triplets, hyper-relational facts consisting of a base triplet and an arbitrary …