Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …

There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

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 …

Large language model (LLM) based knowledge graph completion (KGC) aims to predict the
missing triples in the KGs with LLMs. However, research about LLM-based KGC fails to …

In recent years, pre-trained language models (PLMs) have been shown to capture factual
knowledge from massive texts, which encourages the proposal of PLM-based knowledge …

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 Factorisation-based Models (FMs), such as DistMult, have enjoyed enduring
success for Knowledge Graph Completion (KGC) tasks, often outperforming Graph Neural …

Abstract Knowledge graph (KG) reasoning utilizes two primary techniques, ie, rule-based
and KG-embedding based. The former provides precise inferences, but inferring via …

A fundamental task for knowledge graphs (KGs) is knowledge graph completion (KGC). It
aims to predict unseen edges by learning representations for all the entities and relations in …

Conventional representation learning algorithms for knowledge graphs (KG) map each
entity to a unique embedding vector. Such a shallow lookup results in a linear growth of …