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 …

In recent years, Knowledge Graphs (KGs) have played a crucial role in the development of
advanced knowledge-intensive applications, such as recommender systems and semantic …

Many mathematical models have been leveraged to design embeddings for representing
Knowledge Graph (KG) entities and relations for link prediction and many downstream tasks …

Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities
between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend …

Entity alignment (EA) aims at finding equivalent entities in different knowledge graphs (KGs).
Embedding-based approaches have dominated the EA task in recent years. Those methods …

Graph alignment, which aims at identifying corresponding entities across multiple networks,
has been widely applied in various domains. As the graphs to be aligned are usually …

Entity Matching (EM), which aims to identify whether two entity records from two relational
tables refer to the same real-world entity, is one of the fundamental problems in data …

Entity Alignment (EA) aims to find equivalent entity pairs between KGs, which is the core
step of bridging and integrating multi-source KGs. In this paper, we argue that existing GNN …

Knowledge Graph (KG) Reasoning plays a vital role in various applications by predicting
missing facts from existing knowledge. Inductive KG reasoning approaches based on Graph …

Full-graph training on graph neural networks (GNN) has emerged as a promising training
method for its effectiveness. Full-graph training requires extensive memory and computation …