摘要
没有一个搜索引擎系统在任何情况下所表现出来的性能都比其他的搜索引擎要好,因此研究元搜索引擎是必要的。文中提出了三种元搜索中的传统数据融合方法:基于线性组合的相似度融合、基于排序的Unbiased和Biased-Bayes融合。其中相似度融合通过分析部分Web文档的内容来产生线性组合的参数,Unbiased则将各搜索引擎的结果表均衡地融合在一起,Biased-Bayes则利用了ODP的分类服务和Bayes概率模型来计算文档的相关度。通过实验证明它们是行之有效的融合方法,比较传统的方法的性能有一定提高,在效率上比纯粹分析所有文档的内容来进行融合的方法更好。
As no one research engine surpass any other search engines under all circumstances, and the ＂best＂ system for a particular task may not be known a priori. The Meta - search is an effective way to find relevant documents from the vast source of information in WWW. In this paper, three data fusion methods for the Meta - search have been presented： Similarity Linear Combination, Unbiased and Biased - Bayes. The Biased - Bayes use the ODP directory for priority calculation, and needs few training process. Comparing with other fusion methods, these methods promote the average precision evidently and steadily. They yield improvements in the effectiveness and the effectiveness is comparable to that of approach that analyzing the web documents.
出处
《计算机仿真》
CSCD
2007年第4期120-123,共4页
Computer Simulation
基金
湖北师范学院资助科研项目(2006C10)
