欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
为了提高DeepW eb查询接口匹配的效率和准确率,在现有双重相关性挖掘方法（DCM）的基础上提出了一种用关联挖掘和语义聚类来匹配的方法。在关联挖掘成组属性时,引入一种基于互信息的属性相关度标准,并采用矩阵来实现以解决效率不高问题;在生成同义属性时,提出利用语义网来计算语义相似度,并对属性进行聚类,以生成同义属性。通过在四个领域200多个查询接口上实验,说明改进的方法在效率和准确率方面都比DCM方法有很大提高。
英文摘要：
In order to improve the efficiency and accuracy of Deep Web interface matching, this paper presented a method based on the existing dual correlation mining （DCM） method using association mining and semantic clustering. While digging group attributed by using correlation algorithm, introduced and realized a new correlation measure based on mutual information by matrix to resolve the inefficiency problem. Clustered the attributes to synonymous attributes by their similarity which was computed by using semantic net. By the comparison on more than 200 interfaces in 4 domains, the experiment results indicate that the improved method has greatly heighted than DCM in the respect of efficiency and accuracy.
同期刊论文项目
同项目期刊论文
期刊信息
位置：