文献类型：期刊文献

中文题名：KernelRank： Exploiting Semantic Linkage Kernels for Relevant Pages Finding

英文题名：KernelRank: Exploiting Semantic Linkage Kernels for Relevant Pages Finding

作者：Wang Yaowei[1];Su Limin[2];Tian Yonghong[3]

第一作者：Wang Yaowei

通讯作者：Wang, YW[1]

机构：[1]Beijing Inst Technol, Dept Elect Engn, Beijing 100081, Peoples R China;[2]Beijing Union Univ, Informat Sch, Beijing 100100, Peoples R China;[3]Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China

第一机构：Beijing Inst Technol, Dept Elect Engn, Beijing 100081, Peoples R China

通讯机构：[1]corresponding author), Beijing Inst Technol, Dept Elect Engn, Beijing 100081, Peoples R China.

年份：2009

卷号：18

期号：3

起止页码：405-410

中文期刊名：电子学报：英文版

外文期刊名：CHINESE JOURNAL OF ELECTRONICS

收录：CSTPCD;;EI(收录号：20093612286387);Scopus(收录号：2-s2.0-69249193145);WOS:【SSCI(收录号:WOS:000268110500004)，SCI-EXPANDED(收录号:WOS:000268110500004)】；

语种：英文

中文关键词：网页地址;语义;连杆;网络数据;现实世界;超链接;算法;页面

外文关键词：KnernalRank; Machine learning; Semantic link

摘要：Relevant pages finding is to find a set of relevant pages that address the same topic as the given page. Hyperlink relationship is an important useful clue for this task. Some hyperlinks are useful, also some are irrelevant or noisy. Therefore, it is important to design efficient relevant pages finding methods that can work well in the real-world Web data. In this paper, we propose a relevant pages finding algorithm, KernelRank. This algorithm takes advantage of linkage kernels to reveal latent semantic relationships among pages and to identify relevant pages precisely and effectively. Experiments are conducted on WT10G and the results show that the KernelRank algorithm is feasible and effective.