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 …

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 is a viable means for integrating heterogeneous knowledge among different
knowledge graphs (KGs). Recent developments in the field often take an embedding-based …

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 …

Structural heterogeneity between knowledge graphs is an outstanding challenge for entity
alignment. This paper presents Neighborhood Matching Network (NMN), a novel entity …

Knowledge embeddings (KE) represent a knowledge graph (KG) by embedding entities and
relations into continuous vector spaces. Existing methods are mainly structure-based or …

The multi-modal entity alignment (MMEA) aims to find all equivalent entity pairs between
multi-modal knowledge graphs (MMKGs). Rich attributes and neighboring entities are …