Sep 5, 2021 · In this paper, we propose a novel link prediction method called kernel multi-attention neural network for knowledge graph embedding (KMAE) which ...
Knowledge graph embedding, which aims to represent entities and relations in vector spaces, has shown outstanding performance on a few knowledge graph ...
Mar 6, 2023 · Knowledge graph embedding is a popular method to solve the incompleteness of knowledge graph. At present, research on knowledge graph ...
Kernel multi-attention neural network for knowledge graph embedding
www.ikcest.org › journal-11304942
In this paper, we propose a novel link prediction method called kernel multi-attention neural network for knowledge graph embedding (KMAE) which is able to ...
In this paper, we propose a novel dynamic convolutional embedding model ConvD for knowledge graph completion, which directly reshapes the relation embeddings ...
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Jul 9, 2023 · Highly efficient knowledge graph embedding learning with orthogonal procrustes anal- ysis. ... Relational graph neural network with hierarchical ...
Knowledge Based Systems 2021. paper. (KMAE) Dan Jiang, Ronggui Wang, Juan Yang, Lixia Xue. "Kernel multi-attention neural network for knowledge graph embedding" ...
Jiang, Kernel multi-attention neural network for knowledge graph embedding, Knowledge-Based Systems, № 227 ... knowledge graph completion model based on multiple ...
For this reason, this paper combines the self-attention mechanism with a KG-based model. It constructs HSTrHouse, a hierarchical self-attentive embedding model ...
ParamE: Regarding Neural Network Parameters as Relation ...
www.semanticscholar.org › paper › Para...
A new knowledge graph embedding model called ParamE which can utilize translational properties and expressiveness together and can significantly outperform ...