Feb 2, 2020 · This paper proposes a knowledge alignment neural network named KANN for multilingual knowledge graphs.

Abstract. Many recent works have demonstrated the bene- fits of knowledge graph embeddings in complet- ing monolingual knowledge graphs. Inasmuch as.

Specifically, we first build a relational graph neural network by sharing the embeddings of aligned nodes to transfer language-independent knowledge.

Aug 3, 2020 · Knowledge graph (KG) is a different structure then Graph Neural Network (GNN). Both are indeed graphs but where KG differs is that it is not a Machine learning ...

In this paper, we propose a neural network based model, named DeepE, to address the problem, which stacks multiple building blocks to predict the tail entity.

Sep 1, 2021 · Bibliographic details on Multilingual Knowledge Graph Embeddings with Neural Networks.

In this paper, we propose a Shared Embedding based Neural Network (SENN) model for KGC. It integrates the prediction tasks of head entities, relations and tail ...

This article introduces a triple-attention-based multi-channel CNN model, named ConvAMC, for the KGE task.

Jul 11, 2023 · The main goal of knowledge graph embedding techniques is to create a dense representation of the graph (ie, embed the graph) in a continuous, low-dimensional ...

This study presents an innovative framework, namely learning knowledge graph embedding with a dual-attention embedding network (D-AEN), to jointly propagate ...