Knowledge Base Enhancement via Data Facts and Crowdsourcing

Recently, knowledge base systems such as Freebase, YAGO, etc. have been designed and widely applied while most of the knowledge bases are far from being of a high quality. According to the recent researches, the low quality is mainly caused by the loss and low accuracy of the RDF triples, which are the main components of knowledge base systems. In this paper, we propose approaches to enhance the RDF triples in knowledge bases, which is significant for providing good information retrieval service. Specifically, we utilize data facts stored in database systems to obtain possible updates for knowledge bases. Furthermore, inspired by the popular and successful applications of crowdsourcing platforms, we explore the use of crowdsourcing to verify the updates. We propose KD graph to model the possible updates and design a comprehensive framework for knowledge base enhancement problem. Since crowdsourcing employs human power and requires expenditure, we propose an optimal and dynamic method to select candidates for crowdsourcing within a limited budget so that the benefit of enhancing the knowledge base can be maximized. To reduce the time cost, we adopt split techniques and design Simple Split(SS) and Dynamic Split(DS) algorithms. We verify the effectiveness of our solutions by conducting crowdsourcing simulation experiments and experiments on a crowdsourcing platform namely gMission.