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 …

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 …

Graph neural networks (GNNs) have emerged as a powerful paradigm for embedding-
based entity alignment due to their capability of identifying isomorphic subgraphs. However …

Entity alignment seeks to find entities in different knowledge graphs (KGs) that refer to the
same real-world object. Recent advancement in KG embedding impels the advent of …

This work presents a two-stage neural architecture for learning and refining structural
correspondences between graphs. First, we use localized node embeddings computed by a …

The booming of multi-modal knowledge graphs (MMKGs) has raised the imperative demand
for multi-modal entity alignment techniques, which facilitate the integration of multiple …

Entity alignment aims to identify equivalent entity pairs from different Knowledge Graphs
(KGs), which is essential in integrating multi-source KGs. Recently, with the introduction of …

This work studies the use of visual semantic representations to align entities in
heterogeneous knowledge graphs (KGs). Images are natural components of many existing …

Entity alignment to find equivalent entities in cross-lingual Knowledge Graphs (KGs) plays a
vital role in automatically integrating multiple KGs. Existing translation-based entity …

Entity alignment, aiming to identify equivalent entities across different knowledge graphs
(KGs), is a fundamental problem for constructing Web-scale KGs. Over the course of its …