Towards Confidence in the Truth: A Bootstrapping based Truth Discovery Approach
Houping Xiao*, SUNY Buffalo; Jing Gao, ; Qi Li, SUNY Buffalo; Fenglong Ma, SUNY Buffalo; Lu Su, SUNY Buffalo; Yunlong Feng, KU Leuven; Aidong Zhang,
The demand for automatic extraction of true information (i.e., truths) from conﬂicting multi-source data has soared recently. A variety of truth discovery methods have witnessed great successes via jointly estimating source reliability and truths. All existing truth discovery methods focus on providing a point estimator for each object’s truth, but in many real-world applications, conﬁdence interval estimation of truths is more desirable, since conﬁdence interval contains richer information. To address this challenge, in this paper, we propose a novel truth discovery method (ET-CIBoot) to construct conﬁdence interval estimates as well as identify truths, where the bootstrapping techniques are nicely integrated into the truth discovery procedure. Due to the properties of bootstrapping, the estimators obtained by ETCIBoot are more accurate and robust compared with the state-of-the-art truth discovery approaches. Theoretically, we prove the asymptotical consistency of the conﬁdence interval obtained by ETCIBoot. Experimentally, we demonstrate that ETCIBoot is not only eﬀective in constructing conﬁdence intervals but also able to obtain better truth estimates.
Filed under: Data Reliability and Truthfulness