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 …
Generative adversarial network for unsupervised multi-lingual knowledge graph entity alignment
Entity alignment is an essential process in knowledge graph (KG) fusion, which aims to link
entities representing the same real-world object in different KGs, to achieve entity expansion …
entities representing the same real-world object in different KGs, to achieve entity expansion …
[PDF][PDF] CPa-WAC: constellation partitioning-based scalable weighted aggregation composition for knowledge graph embedding
Scalability and training time are crucial for any graph neural network model processing a
knowledge graph (KG). While partitioning knowledge graphs helps reduce the training time …
knowledge graph (KG). While partitioning knowledge graphs helps reduce the training time …
Investigating Graph Structure Information for Entity Alignment with Dangling Cases
Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs
(KGs), which play an important role in knowledge engineering. Recently, EA with dangling …
(KGs), which play an important role in knowledge engineering. Recently, EA with dangling …
HybEA: Hybrid Attention Models for Entity Alignment
N Fanourakis, F Lekbour, V Efthymiou… - arXiv preprint arXiv …, 2024 - arxiv.org
The proliferation of Knowledge Graphs (KGs) that support a wide variety of applications, like
entity search, question answering and recommender systems, has led to the need for …
entity search, question answering and recommender systems, has led to the need for …
Advancing entity alignment with dangling cases: a structure-aware approach through optimal transport learning and contrastive learning
Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs
(KGs), which plays an important role in knowledge engineering. Recently, EA with dangling …
(KGs), which plays an important role in knowledge engineering. Recently, EA with dangling …
Structural bias in knowledge graphs for the entity alignment task
Abstract Knowledge Graphs (KGs) have recently gained attention for representing
knowledge about a particular domain and play a central role in a multitude of AI tasks like …
knowledge about a particular domain and play a central role in a multitude of AI tasks like …
Study of Topology Bias in GNN-based Knowledge Graphs Algorithms
Graph neural networks (GNN) have recently been integrated into knowledge graph
representation learning. The efficient message-passing functions in GNNs capture latent …
representation learning. The efficient message-passing functions in GNNs capture latent …
Knowledge Graphs can play together: Addressing knowledge graph alignment from ontologies in the biomedical domain
H Abi Akl, D Mariko, YA Pilatte, S Durfort… - … Retrieval (KDIR 2024), 2024 - hal.science
We introduce DomainKnowledge, a system that leverages a pipeline for triple extraction
from natural text and domain-specific ontologies leading to knowledge graph construction …
from natural text and domain-specific ontologies leading to knowledge graph construction …
HybridGCN: An Integrative Model for Scalable Recommender Systems with Knowledge Graph and Graph Neural Networks.
S Kha, TV Le - … Journal of Advanced Computer Science & …, 2024 - search.ebscohost.com
Abstract Graph Neural Networks (GNNs) have emerged as a state-of-the-art approach in
building modern Recommender Systems (RS). By leveraging the complex relationships …
building modern Recommender Systems (RS). By leveraging the complex relationships …