Empowering Truth Discovery with Multi-Truth Prediction

Abstract

Truth discovery is the problem of detecting true values from the conflicting data provided by multiple sources on the same data items. Since sources' reliability is unknown a priori, a truth discovery method usually estimates sources' reliability along with the truth discovery process. A major limitation of existing truth discovery methods is that they commonly assume exactly one true value on each data item and therefore cannot deal with the more general case that a data item may have multiple true values (or multi-truth). Since the number of true values may vary from data item to data item, this requires truth discovery methods being able to detect varying numbers of truth values from the multisource data. In this paper, we propose a multi-truth discovery approach, which addresses the above challenges by providing a generic framework for enhancing existing truth discovery methods. In particular, we redeem the numbers of true values as an important clue for facilitating multi-truth discovery. We present the procedure and components of our approach, and propose three models, namely the byproduct model, the joint model, and the synthesis model to implement our approach. We further propose two extensions to enhance our approach, by leveraging the implications of similar numerical values and values' co-occurrence information in sources' claims to improve the truth discovery accuracy. Experimental studies on real-world datasets demonstrate the effectiveness of our approach.

Beyond facts – a survey and conceptualisation of claims in online discourse analysis

“…"Truth Discovery aims at identifying facts (true claims) when conflicting claims are made by several sources" [18]. In this domain, the terms data items and truths are used to refer to invalidated mentions of knowledge and the true values respectively [202,209].…”

Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking

“…For example, [111] leverages trustworthiness and clustering algorithms to provide a trust discovery mechanism that also relies on problem scale reduction to achieve scalability. However, as outlined in [116][117][118] truth might not be unique and multiple truth could exist. Accordingly, proper truth discovery mechanism must be designed by exploiting, for example, Bayesian approaches [116] or predictions and implications [117].…”

“…However, as outlined in [116][117][118] truth might not be unique and multiple truth could exist. Accordingly, proper truth discovery mechanism must be designed by exploiting, for example, Bayesian approaches [116] or predictions and implications [117]. Given the huge amount of data generated by the IoT network, data mining and machine learning approaches can be proficiently leveraged to provide improved services to citizens such as traffic management and e-health monitoring [119].…”