The Internet now is a large-scale platform with big data. Finding truth from a huge dataset has attracted extensive attention, which can maintain the quality of data collected by users and provide users with accurate ...The Internet now is a large-scale platform with big data. Finding truth from a huge dataset has attracted extensive attention, which can maintain the quality of data collected by users and provide users with accurate and efficient data. However, current truth finder algorithms are unsatisfying, because of their low accuracy and complication. This paper proposes a truth finder algorithm based on entity attributes (TFAEA). Based on the iterative computation of source reliability and fact accuracy, TFAEA considers the interactive degree among facts and the degree of dependence among sources, to simplify the typical truth finder algorithms. In order to improve the accuracy of them, TFAEA combines the one-way text similarity and the factual conflict to calculate the mutual support degree among facts. Furthermore, TFAEA utilizes the symmetric saturation of data sources to calculate the degree of dependence among sources. The experimental results show that TFAEA is not only more stable, but also more accurate than the typical truth finder algorithms.展开更多
基金supported by the National Natural Science Foundation of China(61472192)the Scientific and Technological Support Project(Society)of Jiangsu Province(BE2016776)
文摘The Internet now is a large-scale platform with big data. Finding truth from a huge dataset has attracted extensive attention, which can maintain the quality of data collected by users and provide users with accurate and efficient data. However, current truth finder algorithms are unsatisfying, because of their low accuracy and complication. This paper proposes a truth finder algorithm based on entity attributes (TFAEA). Based on the iterative computation of source reliability and fact accuracy, TFAEA considers the interactive degree among facts and the degree of dependence among sources, to simplify the typical truth finder algorithms. In order to improve the accuracy of them, TFAEA combines the one-way text similarity and the factual conflict to calculate the mutual support degree among facts. Furthermore, TFAEA utilizes the symmetric saturation of data sources to calculate the degree of dependence among sources. The experimental results show that TFAEA is not only more stable, but also more accurate than the typical truth finder algorithms.
