期刊论文

Liu, H

英文

Expert Systems with Applications

0957-4174

2023

233

120953

10.1016/j.eswa.2023.120953

This work was supported by the National Natural Science Foundation of China under Grant 62211530433, Grant 62277041, 62203024, Grant 62177018, Grant 62177019 Grant 62173286, Grant 92167102, Grant 62077020, and Grant 62005092, and in part by the Fundamental Research Funds for the Central Universities under Grant CCNU20ZT017 and Grant CCNU2020ZN008.

本校为第一且通讯机构

Knowledge graph embedding (KGE) aims to complete link prediction tasks effectively by learning the representation of entity and relation. Recently, deep neural networks have achieved prominent results for learning KGE. However, knowledge graphs typically contain massive information about entities and relations, and existing deep neural network-based KGE models exploit semantic information from simple explicit feature concatenation and reshaping without considering bidirectional implicit interactions, which cannot accentuate relevant predictive ...