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 …

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 …

Foundation models in language and vision have the ability to run inference on any textual
and visual inputs thanks to the transferable representations such as a vocabulary of tokens …

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 …

Knowledge graphs (KGs) have become valuable knowledge resources in various
applications, and knowledge graph embedding (KGE) methods have garnered increasing …

Current Multimodal Knowledge Graph Construction (MKGC) models struggle with the real-
world dynamism of continuously emerging entities and relations, often succumbing to …

Inductive knowledge graph completion (KGC) aims to infer the missing relation for a set of
newly-coming entities that never appeared in the training set. Such a setting is more in line …

Complex logical query answering (CLQA) in knowledge graphs (KGs) goes beyond simple
KG completion and aims at answering compositional queries comprised of multiple …

Embedding based Knowledge Graph (KG) Completion has gained much attention over the
past few years. Most of the current algorithms consider a KG as a multidirectional labeled …