scholar.google.com › citations
Through adaptive negative sampling, we are able to effectively utilize negative samples for model training, thereby enhancing the accuracy and efficiency of ...
In this study, we apply adaptive negative sampling to the classical knowledge graph embedding model transE. We have performed optimization on the loss function.
Adaptive NS [84] adapts the negative sampling process based on the frequency of positive samples, assigning more negative samples to low-frequency triples. By ...
Oct 10, 2024 · We propose a generative adversarial approach called Diversified and Adaptive Negative Sampling DANS on knowledge graphs.
Missing: Subsampling. | Show results with:Subsampling.
Sep 13, 2019 · Knowledge graph embedding aims at embedding entities and relations in a knowledge graph into a continuous, dense, low-dimensional and ...
Missing: via Subsampling.
In the context of knowledge graph embedding, negative triplets are often constructed by replacing the tail or head entity in a positive triplet with a ...
Missing: Subsampling. | Show results with:Subsampling.
Knowledge graph embedding (KGE) aims to map entities and relations of a knowledge graph (KG) into a low-dimensional and dense vector space via contrasting ...
Missing: Subsampling. | Show results with:Subsampling.
Oct 11, 2024 · DANS is equipped with a two-way generator that generates more diverse negative triplets through two pathways, and an adaptive mechanism that ...
A method named adaptive negative sampling (ANS), combined with the TransE model, generated a set of high-quality negative triples which benefit for ...
Missing: Subsampling. | Show results with:Subsampling.
May 22, 2022 · Knowledge graph embedding aims to represent entities and relations as low-dimensional vec- tors, which is an effective way for predicting.
Missing: Subsampling. | Show results with:Subsampling.