[HTML][HTML] GeoNLU: Bridging the gap between natural language and spatial data infrastructures
Integrating natural language processing (NLP) techniques with spatial data infrastructures
(SDIs) potentially revolutionize the way users interact with geospatial data. This article …
(SDIs) potentially revolutionize the way users interact with geospatial data. This article …
Complete feature learning and consistent relation modeling for few-shot knowledge graph completion
J Liu, CF Fan, F Zhou, H Xu - Expert Systems with Applications, 2024 - Elsevier
Few-shot knowledge graph completion focuses on predicting unseen facts of long-tail
relations in knowledge graphs with only few reference sets. The key challenge for tackling …
relations in knowledge graphs with only few reference sets. The key challenge for tackling …
CliqueFluxNet: unveiling EHR insights with stochastic edge fluxing and maximal clique utilisation using graph neural networks
Abstract Electronic Health Records (EHRs) play a crucial role in shaping predictive are
models, yet they encounter challenges such as significant data gaps and class imbalances …
models, yet they encounter challenges such as significant data gaps and class imbalances …
ConeE: Global and local context-enhanced embedding for inductive knowledge graph completion
Abstract Knowledge graph completion (KGC) aims at completing missing information in
knowledge graphs (KGs). Most previous works work well in the transductive setting, but are …
knowledge graphs (KGs). Most previous works work well in the transductive setting, but are …
Disentangled Relational Graph Neural Network with Contrastive Learning for knowledge graph completion
Learning disentangled entity representations has garnered significant attention in the field of
knowledge graph completion (KGC). However, the existing methods inherently overlook the …
knowledge graph completion (KGC). However, the existing methods inherently overlook the …
Multi-view semantic enhancement model for few-shot knowledge graph completion
R Ma, H Wu, X Wang, W Wang, Y Ma, L Zhao - Expert Systems with …, 2024 - Elsevier
In recent years, few-shot knowledge graph completion (FKGC) has gained popularity as a
solution to the long-tail distribution problem of real-world knowledge graphs (KGs). The …
solution to the long-tail distribution problem of real-world knowledge graphs (KGs). The …
High-Order Neighbors Aware Representation Learning for Knowledge Graph Completion
As a building block of knowledge acquisition, knowledge graph completion (KGC) aims at
inferring missing facts in knowledge graphs (KGs) automatically. Previous studies mainly …
inferring missing facts in knowledge graphs (KGs) automatically. Previous studies mainly …
A novel dynamic risk assessment method for hazardous chemical warehouses based on improved SVM and mathematical methodologies
Effective dynamic risk assessment is crucial for identifying process hazards and preventing
accidents. The rapid development of modern technology urgently requires the development …
accidents. The rapid development of modern technology urgently requires the development …
Fair large kernel embedding with relation-specific features extraction for link prediction
Q Zhang, S Huang, Q Xie, F Zhao, G Wang - Information Sciences, 2024 - Elsevier
Abstract Knowledge graph embedding is a crucial technique for addressing the challenge of
incomplete knowledge graphs, and convolutional neural networks are widely applied in this …
incomplete knowledge graphs, and convolutional neural networks are widely applied in this …
Implicit relational attention network for few-shot knowledge graph completion
XH Yang, QY Li, D Wei, HX Long - Applied Intelligence, 2024 - Springer
Abstract Knowledge Graphs can not contain all the knowledge during the construction
process, so needs to be completed to enhance its integrity. In real knowledge graphs …
process, so needs to be completed to enhance its integrity. In real knowledge graphs …