基于网页上下文的Deep Web数据库分类

讨论了提高Deep Web数据库分类准确性的若干新技术,其中包括利用HTML网页的内容文本作为理解数据库内容的上下文和把数据库表的属性标记词归一的过程.其中对网页中的内容文本的发现算法是基于对网页文本块的多种统计特征.而对数据库属性标记词的归一过程是把同义标记词用代表词进行替代的过程.给出了采用分层模糊集合对给定学习实例所发现的领域和语言知识进行表示和基于这些知识对标记词归一化算法.基于上述预处理,给出了计算Deep Web数据库的K-NN(k nearest neighbors)分类算法,其中对数据库之间语义距离计算综合了数据库表之间和含有数据库表的网页的内容文本之间的语义距离.分类实验给出算法对未预处理的网页和经过预处理后的网页在数据库分类精度、查全率和综合F1等测度上的分类结果比较.

Classification of Deep Web Databases Based on the Context of Web Pages

New techniques are discussed for enhancing the classification precision of deep Web databases, which include utilizing the content texts of the HTML pages containing the database entry forms as the context and a unification processing for the database attribute labels. An algorithm to find out the content texts in HTML pages is developed based on multiple statistic characteristics of the text blocks in HTML pages. The unification processing for database attributes is to let the attribute labels that are closed semantically be replaced with delegates. The domain and language knowledge found in learning samples is represented in hierarchical fuzzy sets and an algorithm for the unification processing is proposed based on the presentation. Based on the pre-computing a k-NN (k nearest neighbors) algorithm is given for deep Web database classification, where the semantic distance between two databases is calculated based on both the distance between the content texts of the HTML pages and the distance between database forms embedded in the pages. Various classification experiments are carried out to compare the classification results done by the algorithm with pre-computing and the one without the pre-computing in terms of classification precision, recall and F1 values.