Denial-Constraint-Based Truth Discovery for Isomorphic Data

Aggregating accurate information from multi-source conflicting data is crucial. A common approach to address this problem is Voting/Averaging. However, such methods usually fail to achieve correct results, since they assume that all the sources are equally reliable. In most cases, the information quality usually varies a lot among diversified sources, due to the existence of different levels of errors such as recording errors, outdated data, and even intentional errors in each source. Based on the above observation, a research topic named truth discovery has been proposed. Considering relations among entities and attributes are commonly existing in the real-world applications, in this chapter, we introduce the constrained truth discovery problem [1]. We incorporate denial constraints, a universally quantified first-order logic formalism which can express a large number of effective and widely existing relations among entities, into the process of truth discovery. Specifically, we give a motivate example and define the problem in Sects. 3.1 and 3.2, respectively. In Sect. 3.3, we investigate the constrained optimization problem and provide solutions to the optimization problem. Finally, we conclude this chapter in Sect. 3.4.