InGram: Inductive knowledge graph embedding via relation graphs
Inductive knowledge graph completion has been considered as the task of predicting
missing triplets between new entities that are not observed during training. While most …
missing triplets between new entities that are not observed during training. While most …
Generalizing to unseen elements: A survey on knowledge extrapolation for knowledge graphs
Knowledge graphs (KGs) have become valuable knowledge resources in various
applications, and knowledge graph embedding (KGE) methods have garnered increasing …
applications, and knowledge graph embedding (KGE) methods have garnered increasing …
Exploring multi-granularity contextual semantics for fully inductive knowledge graph completion
J Wang, W Li, AM Luvembe, X Yu, X Zhang… - Expert Systems with …, 2025 - Elsevier
Fully inductive knowledge graph completion (KGC) aims to predict triplets involving both
unseen entities and relations. Recent several approaches transform paths between entities …
unseen entities and relations. Recent several approaches transform paths between entities …
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 …
A comparative analysis of computational drug repurposing approaches: proposing a novel tensor-matrix-tensor factorization method
Efficient drug discovery relies on drug repurposing, an important and open research field.
This work presents a novel factorization method and a practical comparison of different …
This work presents a novel factorization method and a practical comparison of different …
A knowledge graph embedding model based on multi-level analogical reasoning
X Zhao, M Yang, H Yang - Cluster Computing, 2024 - Springer
The existing knowledge graph embedding (KGE) models based on graph neural networks
(GNNs) typically aggregate unreliable neighboring node information, leading to a decrease …
(GNNs) typically aggregate unreliable neighboring node information, leading to a decrease …
[PDF][PDF] Prévision de la demande d'électricité par régression linéaire et réseaux de neurones artificiels: application au réseau de la ville de Baie-Comeau
Y Akrour - 2022 - depot-e.uqtr.ca
Résumé En raison de la croissance démographique et du développement économique, le
secteur d'énergie et des réseaux électriques au nord du Canada est dans l'obligation de …
secteur d'énergie et des réseaux électriques au nord du Canada est dans l'obligation de …