Towards Confidence Interval Estimation in Truth Discovery
Towards Confidence Interval Estimation in Truth Discovery
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DOI：
10.1109/tkde.2018.2837026
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发表时间：
2019-03
影响因子：
8.9
通讯作者：
Houping Xiao;Jing Gao;Qi Li;Fenglong Ma;Lu Su;Yunlong Feng;Aidong Zhang
中科院分区：
文献类型：
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作者：
Houping Xiao;Jing Gao;Qi Li;Fenglong Ma;Lu Su;Yunlong Feng;Aidong Zhang
自动提取真实信息的需求（即，事实）的数据最近飙升。多种真相发现方法通过联合评估来源可靠性和真相而取得了巨大成功。现有的真值发现方法都是为每个对象的真值提供一个点估计器，但在实际应用中，置信区间包含了更丰富的信息，因此对真值的置信区间估计更为理想。为了解决这一挑战，在本文中，我们提出了一种新的真理发现方法（ETCIBoot），以构建置信区间估计以及识别真理，其中自举技术很好地集成到真理发现过程中。由于自举的性质，ETCIBoot得到的估计是更准确和更强大的最先进的真理发现方法相比。所提出的框架进一步适应于处理分布式范式中的大规模真相发现任务。从理论上证明了ETCIBoot方法得到的置信区间的渐近相合性。实验表明，ETCIBoot不仅可以有效地构建置信区间，而且能够获得更好的真值估计。
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 (ETCIBoot) 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. The proposed framework is further adapted to deal with large-scale truth discovery task in distributed paradigm. 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.