7 citations · 13 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 3 cited
Differentiable and Scalable Generative Adversarial Models for Data Imputation
Yangyang Wu, Jun Wang, Xiaoye Miao +2
Data imputation has been extensively explored to solve the missing data problem. The dramatically increasing volume of incomplete data makes the imputation models computationally i…
cs.DB2020★ 7 cited
KGClean: An Embedding Powered Knowledge Graph Cleaning Framework
Congcong Ge, Yunjun Gao, Honghui Weng +3
The quality assurance of the knowledge graph is a prerequisite for various knowledge-driven applications. We propose KGClean, a novel cleaning framework powered by knowledge graph…
cs.DB2019★ 3 cited
A Hybrid Data Cleaning Framework using Markov Logic Networks
Yunjun Gao, Congcong Ge, Xiaoye Miao +3
With the increase of dirty data, data cleaning turns into a crux of data analysis. Most of the existing algorithms rely on either qualitative techniques (e.g., data rules) or quant…