As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in
parallel with the actual industrial processes to offer “Human-Centric” Safe, Secure …

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 …

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 …

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 …

Multi-modal entity alignment (MMEA) aims to discover identical entities across different
knowledge graphs (KGs) whose entities are associated with relevant images. However …

Multi-modal entity alignment aims to identify equivalent entities between two different multi-
modal knowledge graphs, which consist of structural triples and images associated with …

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 …