A survey of multi-modal knowledge graphs: Technologies and trends
In recent years, Knowledge Graphs (KGs) have played a crucial role in the development of
advanced knowledge-intensive applications, such as recommender systems and semantic …
advanced knowledge-intensive applications, such as recommender systems and semantic …
Incorporating context graph with logical reasoning for inductive relation prediction
Relation prediction on knowledge graphs (KGs) aims to infer missing valid triples from
observed ones. Although this task has been deeply studied, most previous studies are …
observed ones. Although this task has been deeply studied, most previous studies are …
Multilingual knowledge graph completion with self-supervised adaptive graph alignment
Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from
complete. Due to labor-intensive human labeling, this phenomenon deteriorates when …
complete. Due to labor-intensive human labeling, this phenomenon deteriorates when …
Knowledge is flat: A seq2seq generative framework for various knowledge graph completion
Knowledge Graph Completion (KGC) has been recently extended to multiple knowledge
graph (KG) structures, initiating new research directions, eg static KGC, temporal KGC and …
graph (KG) structures, initiating new research directions, eg static KGC, temporal KGC and …
Graph attention network with dynamic representation of relations for knowledge graph completion
X Zhang, C Zhang, J Guo, C Peng, Z Niu… - Expert Systems with …, 2023 - Elsevier
Abstract Knowledge graph completion (KGC) aims to predict the missing element in a triple
based on known triples or facts. Recently, plenty of representation learning methods for KGC …
based on known triples or facts. Recently, plenty of representation learning methods for KGC …
An efficiency relation-specific graph transformation network for knowledge graph representation learning
Abstract Knowledge graph representation learning (KGRL) aims to infer the missing links
between target entities based on existing triples. Graph neural networks (GNNs) have been …
between target entities based on existing triples. Graph neural networks (GNNs) have been …
Large-scale graph neural architecture search
Abstract Graph Neural Architecture Search (GNAS) has become a powerful method in
automatically discovering suitable Graph Neural Network (GNN) architectures for different …
automatically discovering suitable Graph Neural Network (GNN) architectures for different …
Acquiring and modeling abstract commonsense knowledge via conceptualization
Conceptualization, or viewing entities and situations as instances of abstract concepts in
mind and making inferences based on that, is a vital component in human intelligence for …
mind and making inferences based on that, is a vital component in human intelligence for …
Clustering-based knowledge graphs and entity-relation representation improves the detection of at risk students
The nature of education has been transformed by technological advances and online
learning platforms, providing educational institutions with more options than ever to thrive in …
learning platforms, providing educational institutions with more options than ever to thrive in …
A knowledge graph completion model based on triple level interaction and contrastive learning
J Hu, H Yang, F Teng, S Du, T Li - Pattern Recognition, 2024 - Elsevier
Abstract Knowledge graphs provide credible and structured knowledge for downstream
tasks such as information retrieval. Nevertheless, the ubiquitous incompleteness of …
tasks such as information retrieval. Nevertheless, the ubiquitous incompleteness of …