Knowledge graph embedding methods for entity alignment: experimental review
In recent years, we have witnessed the proliferation of knowledge graphs (KG) in various
domains, aiming to support applications like question answering, recommendations, etc. A …
domains, aiming to support applications like question answering, recommendations, etc. A …
Selfkg: Self-supervised entity alignment in knowledge graphs
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 …
(KGs), is a fundamental problem for constructing Web-scale KGs. Over the course of its …
Multi-modal contrastive representation learning for entity alignment
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 …
modal knowledge graphs, which consist of structural triples and images associated with …
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 …
Clusterea: Scalable entity alignment with stochastic training and normalized mini-batch similarities
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 …
Embedding-based approaches have dominated the EA task in recent years. Those methods …
A critical re-evaluation of neural methods for entity alignment
Neural methods have become the de-facto choice for the vast majority of data analysis tasks,
and entity alignment (EA) is no exception. Not surprisingly, more than 50 different neural EA …
and entity alignment (EA) is no exception. Not surprisingly, more than 50 different neural EA …
Revisiting embedding-based entity alignment: A robust and adaptive method
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 …
is a critical task in data fusion. In this paper, we revisit existing entity alignment methods in …
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 …
Hierarchical feature aggregation based on transformer for image-text matching
In order to carry out more accurate retrieval across image-text modalities, some scholars use
fine-grained feature to align image and text. Most of them directly use attention mechanism …
fine-grained feature to align image and text. Most of them directly use attention mechanism …
Largeea: Aligning entities for large-scale knowledge graphs
Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs).
Current EA approaches suffer from scalability issues, limiting their usage in real-world EA …
Current EA approaches suffer from scalability issues, limiting their usage in real-world EA …