Approach in High Precision Topic-Specific Resource Discovery on the Web

作     者：YeWei-guo LuZheng-ding 

作者机构：SchoolofComputerScienceandTechnologyHuazhongUniversityofScienceandTechonologyWuhan430074HubeiChina 

出 版 物：《Wuhan University Journal of Natural Sciences》 (武汉大学学报（自然科学英文版）)

年 卷 期：2004年第9卷第1期

页      面：41-45页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：SupportedbytheNationalHigh PerformanceCompu tationFund (0 0 30 3) 

主　　题：HITS evolution web graph power law distribution context analysis 

摘      要：The Internet presents numerous sources of useful information nowadays. However, these resources are drowning under the dynamic Web, so accurate finding user-specific information is very difficult. In this paper we discuss a Semantic Graph Web Search (SGWS) algorithm in topic-specific resource discovery on the Web. This method combines the use of hyperlinks, characteristics of Web graph and semantic term weights. We implement the algorithm to find Chinese medical information from the Internet. Our study showed that it has better precision than traditional IR (Information Retrieval) methods and traditional search engines. Key words HITS - evolution web graph - power law distribution - context analysis CLC number TP 391 - TP 393 Foundation item: Supported by the National High-Performance Computation Fund (00303) Biography: Ye Wei-guo (1970-), male, Ph. D candidate, research direction: Web information mining, network security, artificial intelligence.