Knowledge crowdsourcing acceleration

Abstract

Crowdsourcing has recently become a powerful computational tool for data collection and augmentation. Although crowdsourcing has been extensively applied in diverse domains, most tasks are of low complexity such that workers are assumed to be endless, anonymous and disposable. By unlocking the value of human knowledge-related features, e.g., experience, expertise and opinion, we envision that crowdsourcing can reach its full potential to solve complex tasks. We aim at creating a comprehensive theory of crowdsourcing for knowledge creation, i.e., knowledge crowdsourcing, with a focus on developing methods and tools to control and accelerate knowledge creation process. Inspired by previous work, we describe a reference model of knowledge crowdsourcing acceleration, together with three case studies for model validation and extension. The results of our first case study on on-line knowledge creation demonstrate the potential contribution to web engineering.