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 …

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 …

Multimodal Knowledge Graphs (MKGs), which organize visual-text factual knowledge, have
recently been successfully applied to tasks such as information retrieval, question …

Multi-modal knowledge graph embeddings (KGE) have caught more and more attention in
learning representations of entities and relations for link prediction tasks. Different from …

Knowledge graph (KG) technology is a newly emerged knowledge representation method in
the field of artificial intelligence. Knowledge graphs can form logical mappings from cluttered …

Multimodal knowledge graphs (MKGs), which intuitively organize information in various
modalities, can benefit multiple practical downstream tasks, such as recommendation …

Abstract Knowledge graph embeddings are now a widely adopted approach to knowledge
representation in which entities and relationships are embedded in vector spaces. In this …

Side information of items, eg, images and text description, has shown to be effective in
contributing to accurate recommendations. Inspired by the recent success of pre-training …

Visual modality recently has aroused extensive attention in the fields of knowledge graph
and multimedia because a lot of real-world knowledge is multi-modal in nature. However, it …

Multi-modal knowledge graph completion (MMKGC) aims to automatically discover the
unobserved factual knowledge from a given multi-modal knowledge graph by collaboratively …