Knowledge Learning With Crowdsourcing:A Brief Review and Systematic Perspective  

作　　者：Jing Zhang 

机构地区：[1]IEEE [2]School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2022年第5期749-762,共14页自动化学报（英文版）

基　　金：supported by the National Key Research and Development Program of China(2018AAA0102002);the National Natural Science Foundation of China(62076130,91846104).

摘　　要：Big data have the characteristics of enormous volume,high velocity,diversity,value-sparsity,and uncertainty,which lead the knowledge learning from them full of challenges.With the emergence of crowdsourcing,versatile information can be obtained on-demand so that the wisdom of crowds is easily involved to facilitate the knowledge learning process.During the past thirteen years,researchers in the AI community made great efforts to remove the obstacles in the field of learning from crowds.This concentrated survey paper comprehensively reviews the technical progress in crowdsourcing learning from a systematic perspective that includes three dimensions of data,models,and learning processes.In addition to reviewing existing important work,the paper places a particular emphasis on providing some promising blueprints on each dimension as well as discussing the lessons learned from our past research work,which will light up the way for new researchers and encourage them to pursue new contributions.

关 键 词：Crowdsourcing data fusion learning from crowds learning paradigms learning with uncertainty 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]