Abstract Knowledge Graphs (KGs), as a structured human knowledge, manage data in an
ease-of-store, recognizable, and understandable way for machines and provide a rich …

Graphs represent interconnected structures prevalent in a myriad of real-world scenarios.
Effective graph analytics, such as graph learning methods, enables users to gain profound …

Entity alignment (EA) finds equivalent entities that are located in different knowledge graphs
(KGs), which is an essential step to enhance the quality of KGs, and hence of significance to …

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 …

Many AI-related tasks involve the interactions of data in multiple modalities. It has been a
new trend to merge multi-modal information into knowledge graph (KG), resulting in multi …

Entity alignment—the discovery of identical entities across different knowledge graphs (KGs)—
is a critical task in data fusion. In this paper, we revisit existing entity alignment methods in …

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) is the task of identifying the entities that refer to the same real-world
object but are located in different knowledge graphs (KGs). For entities to be aligned …

Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs).
Current EA approaches suffer from scalability issues, limiting their usage in real-world EA …

Entity alignment (EA) is a prerequisite for enlarging the coverage of a unified knowledge
graph. Previous EA approaches either restrain the performance due to inadequate …