Truth Discovery Based on Crowdsourcing

Chen Ye, Hongzhi Wang, Hong Gao, Jianzhong Li, Hui Xie

Abstract: Truth discovery is an important component of data cleaning and information integration. However, in the absence of knowledge, some truth could not be found from databases themselves. A possible solution is to involve crowds to find all the truth with the knowledge of crowds. In this paper, we propose a truth discovery framework based on active learning model with crowdsourcing. First, we give the basic voting algorithm BVote . Then we present the simple crowding-based truth discovery framework STDA based on BVote. Experimental results show that the STDA framework for truth discovery has improved significantly in accuracy with minimal efforts of workers.