Multi-Truth Discovery Method Based on Attribute Fusion
Yang Haolin1,Dong Yongquan1,2(),Chen Huafeng1,Zhang Guoxi1
1School of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221008, China 2Xuzhou Engineering Research Center of Cloud Computing, Xuzhou 221100, China
[Objective] This paper adds influence of auxiliary attributes to the existing models for multi-truth discovery, aiming to improve their F1 values. [Methods] First, we used the auxiliary attributes to calculate the source expertise and consensus degree. Then, we combined the activity degree of multi-truth attribute values to get the degree of support from the source for the conflicting data. Third, we called the existing truth discovery methods to obtain the pseudo tags of the truth. Finally, we used the neural network to capture the complex relationship between the sources and the conflicting data, and identified all truth. [Results] Compared with the sub-optimal model, our method improved the F1 value by 2.25% on the book dataset and by 5.42% on the movie dataset. [Limitations] The proposed method included auxiliary attributes reflecting object features, and more research is needed to explore the impacts of other auxiliary attributes on multi-truth discovery. [Conclusions] The proposed method could effectively discover multi-truth.
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