[1]吕强,宋玲,马军,等.基于本体的Deep Web语义分类研究[J].山东建筑大学学报,2010,(02):118-124.
 LÜ,Qiang,SONG Ling,et al.Research on Deep Web semantic categorization based on ontology[J].Journal of Shandong jianzhu university,2010,(02):118-124.
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基于本体的Deep Web语义分类研究()
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《山东建筑大学学报》[ISSN:1673-7644/CN:37-1449/TU]

卷:
期数:
2010年02期
页码:
118-124
栏目:
研究论文
出版日期:
2010-04-15

文章信息/Info

Title:
Research on Deep Web semantic categorization based on ontology
作者:
吕强1宋玲2 马军3秦英林2
1. 国家电网技术学院,山东 济南 250002; 2.山东建筑大学 计算机科学与技术学院,山东 济南 250101; 3山东大学 计算机科学与技术学院,山东 济南 250101
Author(s):
LÜ Qiang1 SONG Ling2 MA Jun3 et al
1. State Grid Technology College,Jinan 250002, China 2. School of Computer Science and Technology, Shandong Jianzhu University, Jinan 2501013China. School of Computer Science and Technology, Shandong University, Jinan 250101,China
关键词:
Deep Web分类本体语义查询查询探测
Keywords:
Deep Web categorization ontology semantic query query probing
分类号:
TP301
文献标志码:
A
摘要:
针对目前Deep Web分类研究中所采用的Post-query查寻探测方法缺乏语义支持的问题,提出一个基于本体的语义查询探测分类方法。主要思想如下:首先针对一个Deep Web数据库集合,提取查询接口中的属性及其实例,半自动建立领域本体,并且通过领域本体来表示类别特征;然后利用领域本体中的概念以及相应的实例构造语义查询集;最后对待分类的Deep Web数据库利用语义查询集进行查询探测,计算查询探测返回的结果文档在领域本体中的信息覆盖量,并以此对Deep Web进行分类。实验表明:这种语义查询探测分类的方法和以往的方法相比,在准确率、查全率和F1值上有一定的提高。
Abstract:
In view of the problem of lacking semantic support in the Post-query research of Deep Web databases classification, the paper designs a novel semantic query probing classification approach based on ontology. The main idea is as following: Firstly, attributes and instances are extracted from query interface, which are used to build domain ontologies semi-automatically, and characteristics of categories are represented by domain ontologies. Then domain query instances are constructed from domain ontologies, which are used as query probing. Finally coverage degree between returned result documents and domain ontologies are computed, with which Deep Web database is classified. The experiments show that semantic query probing classification method we proposed has improved a lot in precision, recall and F1, compared with classifications before.
更新日期/Last Update: 2010-05-20