From Appearance to Essence

Abstract

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.

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

“…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].…”

Mapping Irrigated Areas in China Using a Synergy Approach

“…Additionally, they showed that the regression method is sensitive to the number of training samples. For China, there is little ground truth data available on irrigation [25]. Hence, we decided to use the agreement-scoring method to create a synergy irrigation map for China.…”