论文标题
众包知识学习：简短的审查和系统的观点
Knowledge Learning with Crowdsourcing: A Brief Review and Systematic Perspective
论文作者
论文摘要
大数据具有巨大的量，高速，多样性，价值 - 比值和不确定性的特征，这会导致知识从他们那里学习。随着众包的出现，可以在按需获得多功能信息，以便很容易参与人群的智慧，以促进知识学习过程。在过去的十三年中，AI社区的研究人员竭尽全力消除人群学习领域的障碍。这份集中的调查论文从系统的角度全面审查了众包学习的技术进步，其中包括数据，模型和学习过程的三个维度。除了回顾现有的重要工作外，该论文还特别强调在每个维度上提供一些有希望的蓝图，并讨论从我们过去的研究工作中学到的经验教训，这将为新研究人员提供道路，并鼓励他们寻求新的贡献。
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.
