.png){kind=icon}
返回
A genetic algorithm based entity resolution approach with active learning
.png){kind=icon}
PRE
AI
DOI：10.1007/s11704-015-5276-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Entity resolution is a key aspect in data quality and data integration, identifying which records correspond to the same real world entity in data sources. Many existing approaches require manually designed match rules to solve the problem, which always needs domain knowledge and is time consuming. We propose a novel genetic algorithm based entity resolution approach via active learning. It is able to learn effective match rules by logically combining several different attributes' comparisons with proper thresholds. We use active learning to reduce manually labeled data and speed up the learning process. The extensive evaluation shows that the proposed approach outperforms the sate-of-the-art entity resolution approaches in accuracy.
Keyword:
entity resolution
genetic algorithm
active learning
data quality
data integration
期刊
IF:
4.6
论文数:
1.6K
被引数:
2.8K
机构
引用论文
分享收藏The complete genome sequence of the Gram-positive bacterium Bacillus subtilis革兰氏阳性细菌枯草芽孢杆菌的全基因组序列.png)
Nature
OAAI
NatureIF0
OAAI分享收藏 没有更多内容
没有更多内容