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 …

Few-shot Learning (FSL) is aimed to make predictions based on a limited number of
samples. Structured data such as knowledge graphs and ontology libraries has been …

Multi-modal entity alignment aims to identify equivalent entities between two different multi-
modal knowledge graphs, which consist of structural triples and images associated with …

Multi-modal entity alignment (MMEA) aims to discover identical entities across different
knowledge graphs (KGs) whose entities are associated with relevant images. However …

The current success of entity alignment (EA) is still mainly based on large-scale labeled
anchor links. However, the refined annotation of anchor links still consumes a lot of …

Entity Alignment (EA) has attracted widespread attention in both academia and industry,
which aims to seek entities with same meanings from different Knowledge Graphs (KGs) …

The entity alignment task aims to align entities corresponding to the same object in different
KGs. The recent work focuses on applying knowledge embedding or graph neural networks …

Automatic taxonomy induction is crucial for web search, recommendation systems, and
question answering. Manual curation of taxonomies is expensive in terms of human effort …

Entity alignment (EA) aims to automatically match entities in different knowledge graphs,
which is beneficial to the development of knowledge-driven applications. Representation …

The majority of currently available entity alignment (EA) solutions primarily rely on structural
information to align entities, which is biased and disregards additional multi-source …