A genetic algorithm based entity resolution approach with active learning  

作　　者：Chenchen SUN Derong SHEN Yue KOU Tiezheng NIE Ge YU 

机构地区：[1]Sohool of Information Science and Engineering, Northeastern University, Shenyang 110819, China

出　　处：《Frontiers of Computer Science》2017年第1期147-159,共13页中国计算机科学前沿（英文版）

基　　金：The authors thank anonymous reviewers for their in- spiting doubts and helpful suggestions during the reviewing process. This work was supported by the National Basic Research Program of China （973 Program） （2012CB316201）, the Fundamental Research Funds for the Cen- tral Universities （N 120816001） and the National Natural Science Foundation of China （Grant Nos. 61472070, 61402213）.

摘　　要：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 ap- proaches 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 en- tity 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 ac- tive 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 res- olution approaches in accuracy.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 ap- proaches 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 en- tity 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 ac- tive 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 res- olution approaches in accuracy.

关 键 词：entity resolution genetic algorithm active learning data quality data integration 

分 类 号：TP391.72[自动化与计算机技术—计算机应用技术] O242.23[自动化与计算机技术—计算机科学与技术]