针对识别分页标签的必要性,提出二种Deep Web结果页面中分页标签识别模型GL(Global Base on Link)和CSL(Commix Baseon Structure and Link).GL是将一个页面的所有超链接Link都抽取出来,然后根据链接探测得到响应页面,分析响应页面的特征来判断是不是分页标签;CSL则是根据分页页面的布局特点,首先缩小分页标签的范围,然后在这个小范围内抽取超链接,最后通过探测方法来确定分页标签的位置,从而抽取出分页标签.通过实验对比,CSL在查全率上略低于GL模型,但是查准率高于GL模型,并且在探测次数上比GL模型降低了一个数量级,所以CSL是一种高效的分页标签抽取模型.
A duplicate identification model is presented to deal with semi-structured or unstructured data extracted from multiple data sources in the deep web.First,the extracted data is generated to the entity records in the data preprocessing module,and then,in the heterogeneous records processing module it calculates the similarity degree of the entity records to obtain the duplicate records based on the weights calculated in the homogeneous records processing module.Unlike traditional methods,the proposed approach is implemented without schema matching in advance.And multiple estimators with selective algorithms are adopted to reach a better matching efficiency.The experimental results show that the duplicate identification model is feasible and efficient.
