Deep Web集成服务的不确定模式匹配

随着Deep Web的迅猛发展,从高度自治、异构及动态变化的Web数据库中,为用户提供高质量的数据逐渐成为当前Deep Web集成服务的一个研究热点.在大部分Web数据库只能通过查询接口为用户提供服务的前提下,如何建立用户请求与集成查询接口模式之间以及集成查询接口模式与Web数据库查询接口模式之间的匹配关系,是Deep Web集成服务中进行合理的用户请求转换的关键.之前的相关工作都是寻找最佳的匹配结果,回避匹配的不确定性,丢弃了可能有价值的其他匹配结果.文中首先剖析了请求转换中模式匹配的不确定性,提出了数字类型的相似度计算方法,给出了进行数字类型的模式匹配的有效的剪枝方法以及数据类型驱动的模式匹配优化方法,并在此基础上提出了一种基于相似度计算的不确定性模式匹配方法,最后通过大量的实验证明了该方法的有效性.

Uncertain Schema Matching in Deep Web Integration Service

With increasing of Deep Web, providing high quality data from autonomous, heterogeneous and dynamic Web databases to users is becoming a hot topic in recent research of Deep Web integration service. How to generate the reasonable schema matching between the keywords of the user request and schema of integrated interface as well as between the schema of integrated interface and that of Web database interface is essential. The related works about schema matching are generating the best schema matching which slide over its uncertainty. This paper analyzes the uncertainty of schema matching, and then proposes a series of similarity measures. To reduce the cost of execution, it proposes the type-based optimization method and schema matching pruning method of numeric data. Based on above analysis, this paper proposes the uncertain schema matching method. The experiments prove the effectiveness and efficiency of the new method.