With the explosive growth of artificial intelligence (AI) and big data, it has become vitally
important to organize and represent the enormous volume of knowledge appropriately. As …

Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …

Recent years have witnessed the resurgence of knowledge engineering which is featured
by the fast growth of knowledge graphs. However, most of existing knowledge graphs are …

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 …

Deep representation learning is a ubiquitous part of modern computer vision. While
Euclidean space has been the de facto standard manifold for learning visual …

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 …

Multi-Modal Entity Alignment (MMEA) is a critical task that aims to identify equivalent entity
pairs across multi-modal knowledge graphs (MMKGs). However, this task faces challenges …

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 …