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...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 pair-wise 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.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61772268the Fundamental Research Funds for the Central Universities of China under Grant Nos.NS2018057 and NJ2018014.
文摘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 pair-wise 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.
