Reliable Truth Discovery for Dynamic and Dependent Sources

Abstract

In the era of Big Data and generative artificial intelligence (AI), discovering the truth about various objects from different sources has become a pressing topic. Existing studies primarily focus on dependent sources with conflicting information, where sources may copy information from each other. However, real-world scenarios are often more complex, with dynamic dependence relationships among sources over time. This complexity makes it much more difficult to discover the truth. One of the key challenges centers on measuring the dynamic dependence among sources. To address this challenge, we have developed three models: <inline-formula><tex-math notation="LaTeX">$Depen\_{S}imple$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>D</mml:mi><mml:mi>e</mml:mi><mml:mi>p</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mo>_</mml:mo><mml:mi>S</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mi>p</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq1-3631376.gif"/></alternatives></inline-formula>, <inline-formula><tex-math notation="LaTeX">$Depen\_{C}omplex$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>D</mml:mi><mml:mi>e</mml:mi><mml:mi>p</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mo>_</mml:mo><mml:mi>C</mml:mi><mml:mi>o</mml:mi><mml:mi>m</mml:mi><mml:mi>p</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq2-3631376.gif"/></alternatives></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$Depen\_{D}ynamic$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>D</mml:mi><mml:mi>e</mml:mi><mml:mi>p</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mo>_</mml:mo><mml:mi>D</mml:mi><mml:mi>y</mml:mi><mml:mi>n</mml:mi><mml:mi>a</mml:mi><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq3-3631376.gif"/></alternatives></inline-formula>. These models are based on the Hidden Markov Model (HMM) and are designed to handle different types of dependencies, namely <italic>simple source dependence</italic>, <italic>complex source dependence</italic>, and <italic>dynamic source dependence</italic>. Based on the constructed models, we propose a generic framework for discovering the latent truth which are evaluated by three HMM-based methods. We conduct extensive experiments on three real-world datasets to evaluate the performance of the proposed methods, and the results demonstrate that all three methods achieve high accuracy over the state-of-the-art methods.