Truth Discovery from Conflicting Multi-Valued Objects

Truth discovery is a fundamental research topic, which aims at identifying the true value(s) of objects of interest given the conflicting multi-sourced data. Although considerable research efforts have been conducted on this topic, we can still point out two significant issues unsolved: i) single-valued assumption, i.e., current methods assume only one true value for each object, while in reality objects with multiple true values widely exist; ii) sparse ground truth, i.e., current works evaluate and compare existing truth discovery methods based on datasets with limited ground truth. Therefore, the empirical studies might be biased and cannot legitimately validate the existing methods. In this PhD project, we propose a full-fledged graph-based model, SmartMTD (Smart Multi-valued Truth Discovery), which incorporates four important implications to conduct truth discovery for multi-valued objects. Two graphs are constructed and further used to derive two aspects of source reliability via random walk computations. We also present a general approach, which utilizes Markov chain models with Bayesian inference, for comparing the existing truth discovery methods and validate our approach without ground truth. Initial empirical studies on two real-world datasets show the effectiveness of SmartMTD.