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中文摘要：
Deep Web中蕴涵了海量的高质量信息.文中从Deep Web数据源的功能属性和非功能属性两个方面对数据源的质量进行度量,建立了一种基于综合模糊评价指标体系的扩展的数据源质量估计模型.实验结果表明该模型得到的数据源质量排序序列和人工排序序列的Kendall’s距离较扩展前有了很大提高,而且质量估计结果也能使数据源的选择得到较高精确度.
英文摘要：
Deep Web contains a mass of high-quality information.This article measures the quality of Deep Web from the data sources functional attributes and non-functional attributes.Then we establish an extended data source quality estimation model based on the synthetic fuzzy evaluation system.The results showed that the Kendall＇s distance between data sources quality sort sequences made by the extended quality estimation model and people has been greatly improved before the expansion.And the estimated results of data sources quality makes the choice of data sources more accurate.
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