Journal of Computer Science and Technology ›› 2020, Vol. 35 ›› Issue (4): 769-793.doi: 10.1007/s11390-020-0350-4

Special Issue: Surveys; Data Management and Data Mining

• Special Section on Entity Resolution • Previous Articles     Next Articles

A Survey on Blocking Technology of Entity Resolution

Bo-Han Li1,2,3, Member, CCF, ACM, Yi Liu1, Student Member, CCF, An-Man Zhang1, Student Member, CCF Wen-Huan Wang1, Student Member, CCF, Shuo Wan1, Student Member, CCF   

  1. 1 College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
    2 Key Laboratory of Safety-Critical Software, Ministry of Industry and Information Technology, Nanjing 211106, China;
    3 Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210000, China
  • Received:2020-01-30 Revised:2020-06-08 Online:2020-07-20 Published:2020-07-20
  • About author:Bo-Han Li received his Ph.D. degree in computer application from Harbin University of Science and Technology, Harbin, in 2009. He is currently an associate professor at the College of Computer Science and Technology of Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing. His current research interests include spatiotemporal database, recommendation system, sentiment analysis, etc. He is a member of CCF and ACM.
  • Supported by:
    This work was partially supported by the National Natural Science Foundation of China under Grant No. 61772268 and the Fundamental Research Funds for the Central Universities of China under Grant Nos. NS2018057 and NJ2018014.

Entity resolution (ER) is a significant task in data integration, which aims to detect all entity profiles that correspond to the same real-world entity. Due to its inherently quadratic complexity, blocking was proposed to ameliorate ER, and it offers an approximate solution which clusters similar entity profiles into blocks so that it suffices to perform pairwise comparisons inside each block in order to reduce the computational cost of ER. This paper presents a comprehensive survey on existing blocking technologies. We summarize and analyze all classic blocking methods with emphasis on different blocking construction and optimization techniques. We find that traditional blocking ER methods which depend on the fixed schema may not work in the context of highly heterogeneous information spaces. How to use schema information flexibly is of great significance to efficiently process data with the new features of this era. Machine learning is an important tool for ER, but end-to-end and efficient machine learning methods still need to be explored. We also sum up and provide the most promising trend for future work from the directions of real-time blocking ER, incremental blocking ER, deep learning with ER, etc.

Key words: blocking construction; blocking optimization; data linkage; entity resolution;

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[1] Sai-Sai Gong, Wei Hu, Wei-Yi Ge, Yu-Zhong Qu. Modeling Topic-Based Human Expertise for Crowd Entity Resolution [J]. Journal of Computer Science and Technology, 2018, 33(6): 1204-1218.
[2] An-Zhen Zhang, Jian-Zhong Li, Hong Gao, Yu-Biao Chen, Heng-Zhao Ma, Mohamed Jaward Bah. CrowdOLA: Online Aggregation on Duplicate Data Powered by Crowdsourcing [J]. , 2018, 33(2): 366-379.
[3] Xue-Li Liu, Hong-Zhi Wang, Jian-Zhong Li, Hong Gao. EntityManager: Managing Dirty Data Based on Entity Resolution [J]. , 2017, 32(3): 644-661.
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