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- research-articleAugust 2023
New attention strategy for negative sampling in knowledge graph embedding
Applied Intelligence (KLU-APIN), Volume 53, Issue 22Nov 2023, Pages 26418–26438https://doi.org/10.1007/s10489-023-04901-0AbstractIn the study of knowledge graph embedding (KGE), self-adversarial negative sampling is a recently proposed technique based on the attention mechanism, which pays more attention to the negative triplets with higher embedding scores. Unfortunately, ...
- research-articleOctober 2022
Learning to recommend journals for submission based on embedding models
Neurocomputing (NEUROC), Volume 508, Issue COct 2022, Pages 242–253https://doi.org/10.1016/j.neucom.2022.08.043AbstractDue to the rapid development of electronic journals, selecting appropriate journals to publish research papers has become a significant challenge to researchers. Sometimes, even a high-quality paper may get rejected from the editor due ...
- research-articleOctober 2022
MRGAT: Multi-Relational Graph Attention Network for knowledge graph completion
Neural Networks (NENE), Volume 154, Issue COct 2022, Pages 234–245https://doi.org/10.1016/j.neunet.2022.07.014AbstractOne of the most effective ways to solve the problem of knowledge graph completion is embedding-based models. Graph neural networks (GNNs) are popular and promising embedding models which can exploit and use the structural information ...
- research-articleSeptember 2022
Knowledge graph embedding with self adaptive double-limited loss
Knowledge-Based Systems (KNBS), Volume 252, Issue CSep 2022https://doi.org/10.1016/j.knosys.2022.109310AbstractMany well-performing embedding models for knowledge graphs employ a negative sampling framework to complete the representation learning in which the loss function is a critical component in distinguishing between positive and negative ...
Highlights- A new loss function, named ADL, for Knowledge graph embedding is proposed.
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