Truth Discovery

Berti‐Équille, Laure

doi:10.1007/978-3-319-63962-8_23-1

Cited by 1 publication

(2 citation statements)

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“…The precision of both methods is 1, as their identified values are all true values, indicating their performance are the same. However, the recall of m 1 is 2 3 and that of m 2 is 1 3 , indicating the performance of m 1 is better than m 2 .…”

Section: Our Approachmentioning

confidence: 96%

“…In their work, a Bayesian based heuristic algorithm is proposed, which computes the probability of each claim being correct given the estimated source weights and the influences between claims. After that, many advanced solutions have been proposed by applying unsupervised or semi-supervised learning techniques while additionally taking various implications of multisourced data into consideration (see [2,22,24,37,51] for relevant surveys).…”

Section: Related Workmentioning

confidence: 99%

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From Appearance to Essence

Fang

Sheng

Wang

et al. 2020

ACM Trans. Intell. Syst. Technol.

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Truth discovery has been widely studied in recent years as a fundamental means for resolving the conflicts in multi-source data. Although many truth discovery methods have been proposed based on different considerations and intuitions, investigations show that no single method consistently outperforms the others. To select the right truth discovery method for a specific application scenario, it becomes essential to evaluate and compare the performance of different methods. A drawback of current research efforts is that they commonly assume the availability of certain ground truth for the evaluation of methods. However, the ground truth may be very limited or even impossible to obtain, rendering the evaluation biased. In this article, we present CompTruthHyp , a generic approach for comparing the performance of truth discovery methods without using ground truth. In particular, our approach calculates the probability of observations in a dataset based on the output of different methods. The probability is then ranked to reflect the performance of these methods. We review and compare 12 representative truth discovery methods and consider both single-valued and multi-valued objects. The empirical studies on both real-world and synthetic datasets demonstrate the effectiveness of our approach for comparing truth discovery methods.

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Section: Our Approachmentioning

confidence: 96%