Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

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

One of the most critical tasks for improving data quality and increasing the reliability of data
analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to …

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 …

Graph Neural Networks (GNNs) have been widely used for the representation learning of
various structured graph data, typically through message passing among nodes by …

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 …

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 …

Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by
linking the equivalent entities from various KGs. GNN-based EA methods present promising …

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