Abstract Knowledge graphs (KGs) model entities or concepts and their relations in a
structural manner. The incompleteness has turned out to be the main challenge that hinders …

Entity alignment (EA) aims at finding equivalent entities in different knowledge graphs (KGs).
Embedding-based approaches have dominated the EA task in recent years. Those methods …

Entity alignment—the discovery of identical entities across different knowledge graphs (KGs)—
is a critical task in data fusion. In this paper, we revisit existing entity alignment methods in …

Entity alignment refers to matching entities with the same realistic meaning in different
knowledge graphs. The structure of a knowledge graph provides the global signal for entity …

Fusing data from different sources to improve decision making in smart cities has received
increasing attention. Collected data through sensors usually exist in a multi-modal form …

Multi-modal entity alignment (MMEA) aims to identify equivalent entities between two multi-
modal knowledge graphs for integration. Unfortunately, prior arts have attempted to improve …

Vehicle trajectory data is essential for traffic management and location-based services.
However, the release of trajectories raises serious privacy concerns because they contain …

Entity alignment is a crucial step in integrating knowledge graphs (KGs) from multiple
sources. Previous attempts at entity alignment have explored different KG structures, such as …

Entity alignment, which is a prerequisite for creating a more comprehensive Knowledge
Graph (KG), involves pinpointing equivalent entities across disparate KGs. Contemporary …

Entity alignment (EA) identifies equivalent entities that locate in different knowledge graphs
(KGs), and has attracted growing research interests over the last few years with the …