基于关键字语义信息的XML文档分类  

Classifying XML documents based on term semantics

作　　者：张利军[1] 李战怀[1] 陈群[1] 娄颖[1] 李宁[1] 

机构地区：[1]西北工业大学计算机科学和技术学院,西安710072

出　　处：《吉林大学学报（工学版）》2012年第6期1510-1514,共5页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(60803043;60970070;61033007);'863'国家高技术研究发展计划项目(2009AA1Z134);'973'国家重点基础研究发展计划项目(2012CB316203)

摘　　要：针对XML数据半结构化的特点及传统的tf-idf方法仅考虑关键字在文档中出现的频率和包含关键字的文档数,而未考虑XML文档中关键字语义信息的不足,提出了一种新的关键字权重度量方法。该方法充分考虑了XML文档中关键字所出现的路径、路径包含关键字的个数、包含路径的文档个数、路径的层次等影响关键字语义的因素,用于计算关键字权重,从而提高了关键字权重度量的准确性。在多个数据集上的实验结果表明,将该方法应用于XML文档的分类时,与传统的tf-idf方法和基于规则的方法相比,分类的查全率、查对率及F1均有所提高。Due to its semi-structured characteristic XML document implies rich term semantics in the structure information.It is inappropriate to measure XML term weight by general Term Frequency-Inverse Document Frequency（TF-IDF） approach.This is because that,when measuring the XML term weight,this approach only considers the term frequency and document frequency but ignores the term semantics implied in the structure information.A novel term weight measuring approach is proposed to overcome the above shortcoming and improve the performance.This new approach takes the factors affecting term semantics into account,such as the paths which contain terms,term frequency in a certain path,frequency of document which contains a certain path,depth of path,etc.Experimental results on several datasets show that,compared with TF-IDF and rule based approaches,the proposed approach can improve the recall,the precision and F1-measure in the classification of XML documents.

关 键 词：计算机软件 半结构化数据 XML挖掘 XML分类 关键字语义 权重度量 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]