基于抽样的Deep Web模式匹配框架  

Deep Web schema matching frame based on sampling

作　　者：袁淼[1] 王鑫[1] 

机构地区：[1]合肥工业大学计算机与信息学院,合肥230009

出　　处：《计算机工程与应用》2015年第3期117-123,共7页Computer Engineering and Applications

基　　金：安徽省自然科学基金(No.090412051)

摘　　要：针对DCM(Dual Correlation Mining)框架匹配特殊模式集时查准率低下的缺陷,借鉴机器学习领域中的bagging方法,提出一种基于抽样的Deep Web模式匹配框架。该框架随机在模式集中抽取多个子模式集,分别对子模式集进行复杂匹配,集成各个子模式集的匹配结果,在整体上提高匹配的查准率。分析与实验证明该框架在处理特殊模式集时,平均能提高查准率41.2%。The dual correlation mining frame has a low precision when some special schemas are in the set. Inspired by bagging algorithm in machine learning, a schema matching frame based on sampling is proposed. The frame randomly sample several subsets form input schemas, then execute the DCM matcher on each subset. The frame will achieve a robust matching accuracy by synthesizing the results of each subset. Experimental results show that the precision is increased by 41.2% in average.

关 键 词：DEEP WEB 模式匹配 相关性挖掘 抽样 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]