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 …

Embedding-based entity alignment represents different knowledge graphs (KGs) as low-
dimensional embeddings and finds entity alignment by measuring the similarities between …

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 is the task of linking entities with the same real-world identity from different
knowledge graphs (KGs), which has been recently dominated by embedding-based …

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 typically suffers from the issues of structural heterogeneity and limited seed
alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model …

The task of entity alignment between knowledge graphs aims to find entities in two
knowledge graphs that represent the same real-world entity. Recently, embedding-based …

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 …

We study the problem of knowledge graph (KG) embedding. A widely-established
assumption to this problem is that similar entities are likely to have similar relational roles …