.png){kind=icon}
返回
A probabilistic ensemble approach for knowledge graph embedding
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2022.06.032.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Knowledge graph embedding (KGE) is a technique for embedding entities and relations of knowledge graphs (KGs) into continuous vector spaces while maintaining the inherent structure of KGs, in this way link prediction can be facilitated by scoring candidate triples. Existing KGE models only capture specific features of a KG, however, link prediction capacity heavily relies on comprehensive features of a KG. In this work, we propose an ensemble approach for KGE by incorporating the features captured by different KGE models. As a key step, we propose a probabilistic scoring index for characterizing the link prediction performance of a given KGE model. Based on this we established a theoretical framework to calculate the optimal parameters for the ensemble model and predict its corresponding link prediction rate. In particular, we proved that our ensemble approach can always improve link prediction performance under some assumptions. The Upper Confidence Bound Algorithm is then used to adjust the parameters. Experimental results on two widely used KGs show that the proposed ensemble approach achieves the state-of-the-art link prediction rate.
Keyword:
Knowledge graph embedding
Ensemble learning
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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
Judo technical-tactical dynamics: analysis of attack system effectiveness in high-level athletes
PREAI
分享收藏Structural context-based knowledge graph embedding for link prediction基于结构化上下文的知识图谱嵌入链接预测NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容