.png){kind=icon}
返回
Multi-Filter soft shrinkage network for knowledge graph embedding
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2024.123875.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Incompleteness is a prominent issue pervasive in real-world knowledge graphs, and link prediction techniques, which utilize known facts to forecast missing or unknown links, have emerged as a critical technology for knowledge graph completion. Recently, convolutional neural network-based knowledge graph embedding (KGE) models have achieved breakthroughs in the field of link prediction by increasing the number of interactions between entities and relations. However, current models mainly emphasize stacking complex architectures to capture more interaction features, ignoring the fact that, as the number of features increases significantly, irrelevant features might overshadow important ones, thus impacting the performance of link prediction. To tackle this problem, this paper introduces a novel KGE model that is based on a multi-filter soft shrinkage network (MFSSN). First, this model adaptively constructs filters based on entity embedding and relationship embedding, thereby increasing the interactions between entities and relationships. Secondly, the soft shrinkage function is innovatively introduced into the model, and a specialized soft shrinkage sub-network is innovatively designed to effectively eliminate noise features and improve attention to important features. Finally, the attention mechanism is added to the network to enhance important features and suppress useless features, improving the ability to utilize features, and thereby enhancing model performance. Through extensive experiments on five benchmark datasets of different sizes, the strong performance and generalization ability of the proposed model are proven. Compared with contrasting methods, it achieves performance leadership in almost all indicators.
Keyword:
Soft shrinkage network
Convolutional neural network
Link prediction
Knowledge graph embedding
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
Impact of Age at Administration, Lysosomal Storage, and Transgene Regulatory Elements on AAV2/8-Mediated Rat Liver Transduction.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏
Novel translation knowledge graph completion model based on 2D convolutionAPPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏分享收藏
查看更多内容