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 …

The Knowledge graph, a multi-relational graph that represents rich factual information
among entities of diverse classifications, has gradually become one of the critical tools for …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

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 …

In the last few years, the interest in knowledge bases has grown exponentially in both the
research community and the industry due to their essential role in AI applications. Entity …

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), which aims to discover equivalent entities in knowledge graphs (KGs),
bridges heterogeneous sources of information and facilitates the integration of knowledge …

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 …

In recent years, we have witnessed the proliferation of knowledge graphs (KG) in various
domains, aiming to support applications like question answering, recommendations, etc. A …

Self-supervised entity alignment (EA) aims to link equivalent entities across different
knowledge graphs (KGs) without the use of pre-aligned entity pairs. The current state-of-the …