Sampling strategies for information extraction over the deep web
作者：
Highlights：
• First large-scale and fine-grained evaluation of query-based sampling techniques.
• Learned keyword queries perform substantially better than queries derived from tuples.
• Focusing on—and processing exhaustively—effective queries leads to high efficiency.
• Focusing on—and processing in rounds—less-effective queries favors quality.
• Filtering underperforming queries favors sampling efficiency but hurts quality.
摘要
•First large-scale and fine-grained evaluation of query-based sampling techniques.•Learned keyword queries perform substantially better than queries derived from tuples.•Focusing on—and processing exhaustively—effective queries leads to high efficiency.•Focusing on—and processing in rounds—less-effective queries favors quality.•Filtering underperforming queries favors sampling efficiency but hurts quality.
论文关键词：Information extraction,Sampling,Deep web,Text mining,Scalability
论文评审过程：Received 25 December 2015, Revised 21 November 2016, Accepted 23 November 2016, Available online 6 December 2016, Version of Record 6 December 2016.
论文官网地址：https://doi.org/10.1016/j.ipm.2016.11.006
