Truth Discovery by Claim and Source Embedding

Lyu, Shanshan; Ouyang, Wentao; Wang, Yongqing; Shen, Huawei; Cheng, Xueqi

doi:10.1109/tkde.2019.2936189

Cited by 17 publications

(3 citation statements)

References 36 publications

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“…Based on these two fundamental assumptions, researchers have proposed numerous truth discovery algorithms. Methods for truth discovery are categorized into two types: single-truth discovery methods [5][6] and multi-truth discovery methods [7][8]. Some methods merge the two [9].…”

Section: Related Workmentioning

confidence: 99%

A Multi-truth Discovery Algorithm based on Statement Value Grouping and Data Source Information Richness

Gao,

Zhang

2024

FSE

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As the volume of data continues to grow, it is common for data from the same source to contain multiple domains. Combining domain segmentation can enhance the effectiveness of data fusion. This paper presents a multi-truth discovery algorithm that utilises statement value grouping and domain information richness. The data is first grouped based on their similarity, and the resulting groups replace the original data. Then, the reliability of each data source is calculated by domain. The truth value and reliability of each source are iteratively calculated until the end condition is met. Finally, the appropriate value is selected as the final result from the obtained dataset. Experiments were conducted on real datasets to demonstrate the algorithm's effectiveness.

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Section: Related Workmentioning

confidence: 99%

A Multi-truth Discovery Algorithm based on Statement Value Grouping and Data Source Information Richness

Gao,

Zhang

2024

FSE

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“…where E q(β k ) [log(β k )] and E q(πn) [log(π n,k )] are computed using (30) and (45) in the sequel, respectively. On the other hand, if A(n, m) = 0, we have (31) in the sequel.…”

Section: Community Membership Indicators Zmentioning

confidence: 99%

“…To mitigate this problem, the Community BCC (CBCC) model was proposed by [29], which grouped agents with similar backgrounds into communities and assumed that the confusion matrix of an agent is a perturbation of the confusion matrix of its community. In [30], the co-occurrence of two agents across different events is considered but communities of agents are not considered in this paper. In [31], the agent correlation is considered by estimating agent reliabilities at sub-type levels instead of event classes.…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Bayesian Neural Network for Truth Discovery in Social Networks

Yang

Tay

2022

IEEE Trans. Knowl. Data Eng.

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The problem of estimating event truths from conflicting agent opinions in a social network is investigated. An autoencoder learns the complex relationships between event truths, agent reliabilities and agent observations. A Bayesian network model is proposed to guide the learning process by modeling the relationship of the autoencoder's outputs with different variables. At the same time, it also models the social relationships between agents in the network. The proposed approach is unsupervised and is applicable when ground truth labels of events are unavailable. A variational inference method is used to jointly estimate the hidden variables in the Bayesian network and the parameters in the autoencoder. Experiments on three real datasets demonstrate that our proposed approach is competitive with, and in most cases better than, several state-of-the-art benchmark methods.

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An End-to-End Deep Neural Network for Truth Discovery

Chen

Dong

et al. 2020

Web Information Systems and Applications

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Truth Discovery by Claim and Source Embedding

Cited by 17 publications

References 36 publications

A Multi-truth Discovery Algorithm based on Statement Value Grouping and Data Source Information Richness

A Multi-truth Discovery Algorithm based on Statement Value Grouping and Data Source Information Richness

An Unsupervised Bayesian Neural Network for Truth Discovery in Social Networks

An End-to-End Deep Neural Network for Truth Discovery

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