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 …

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 …

Building 3D scene graphs has recently emerged as a topic in scene representation for
several embodied AI applications to represent the world in a structured and rich manner …

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

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 …

Entity Alignment (EA) is a crucial task in knowledge fusion, which aims to link entities with
the same real-world identity from different Knowledge Graphs (KGs). Existing methods have …