@conference {
	title = {Trust-Aware Crowdsourcing with Domain Knowledge},
	author = {Liu, Xiangyang., Baras, John S.},
	year = {2015},
	month = {12/2015},
	abstract = {The rise of social network and crowdsourcing platforms~makes it convenient to take advantage of the collective~intelligence to estimate true labels of questions of interest.~However, input from workers is often noisy and even malicious.~Trust is used to model workers in order to better~estimate true labels of questions. We observe that questions~are often not independent in real life applications. Instead,~there are logical relations between them. Similarly, workers~that provide answers are not independent of each other~either. Answers given by workers with similar attributes~tend to be correlated. Therefore, we propose a novel unified~graphical model consisting of two layers. The top layer~encodes domain knowledge which allows users to express~logical relations using first-order logic rules and the bottom~layer encodes a traditional crowdsourcing graphical model.~Our model can be seen as a generalized probabilistic soft~logic framework that encodes both logical relations and~probabilistic dependencies. To solve the collective inference~problem efficiently, we have devised a scalable joint inference~algorithm based on the alternating direction method~of multipliers. Finally, we demonstrate that our model is~superior to state-of-the-art by testing it on multiple real-world~datasets.},
	address = {Osaka, Japan},
	booktitle = {54th Conference on Control (CDC 2015)},
	pages = {pp. 2913-2918}
}
