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

As the volume of data continues to grow, it is common for data from the same source to contain multiple domains. Combining domain segmentation can enhance the effectiveness of data fusion. This paper presents a multi-truth discovery algorithm that utilises statement value grouping and domain information richness. The data is first grouped based on their similarity, and the resulting groups replace the original data. Then, the reliability of each data source is calculated by domain. The truth value and reliability of each source are iteratively calculated until the end condition is met. Finally, the appropriate value is selected as the final result from the obtained dataset. Experiments were conducted on real datasets to demonstrate the algorithm's effectiveness.