Knowledge graphs meet multi-modal learning: A comprehensive survey
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 …
semantic web community's exploration into multi-modal dimensions unlocking new avenues …
Meaformer: Multi-modal entity alignment transformer for meta modality hybrid
Multi-modal entity alignment (MMEA) aims to discover identical entities across different
knowledge graphs (KGs) whose entities are associated with relevant images. However …
knowledge graphs (KGs) whose entities are associated with relevant images. However …
Rethinking uncertainly missing and ambiguous visual modality in multi-modal entity alignment
As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to
identify identical entities across disparate knowledge graphs (KGs) by exploiting associated …
identify identical entities across disparate knowledge graphs (KGs) by exploiting associated …
Mmiea: Multi-modal interaction entity alignment model for knowledge graphs
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 …
increasing attention. Collected data through sensors usually exist in a multi-modal form …
Variety-aware GAN and online learning augmented self-training model for knowledge graph entity alignment
Recently, self-training strategies are adopted in some entity alignment methods, which
address the scarcity of training data by selecting newly-aligned pairs from the predicted …
address the scarcity of training data by selecting newly-aligned pairs from the predicted …
What makes entities similar? A similarity flooding perspective for multi-sourced knowledge graph embeddings
Joint representation learning over multi-sourced knowledge graphs (KGs) yields
transferable and expressive embeddings that improve downstream tasks. Entity alignment …
transferable and expressive embeddings that improve downstream tasks. Entity alignment …
Semi-supervised entity alignment with global alignment and local information aggregation
Entity alignment is a vital task in knowledge fusion, which aims to align entities from different
knowledge graphs and merge them into one single graph. Existing entity alignment models …
knowledge graphs and merge them into one single graph. Existing entity alignment models …
[PDF][PDF] TypeEA: type-associated embedding for knowledge graph entity alignment
Entity alignment is commonly used to link different knowledge graphs and augment facts
about entities. The main objective is to identify the counterpart of a source entity in the target …
about entities. The main objective is to identify the counterpart of a source entity in the target …
Towards semantic consistency: Dirichlet energy driven robust multi-modal entity alignment
In Multi-Modal Knowledge Graphs (MMKGs), Multi-Modal Entity Alignment (MMEA) is crucial
for identifying identical entities across diverse modal attributes. However, semantic …
for identifying identical entities across diverse modal attributes. However, semantic …
Representation learning for entity alignment in knowledge graph: A design space exploration
Entity alignment (EA) is a critical task in knowledge fusion, focusing on identifying equivalent
entities in different knowledge graphs (KGs). As representation learning techniques have …
entities in different knowledge graphs (KGs). As representation learning techniques have …