A MSE Model with Learning Mechanism and Merging Module Based on FCA
Meta search engine improves the coverage of the search results, but its hard to ensure the accuracy of the search results. In order to improve search quality we propose a MSE model with learning mechanism and merging module based on FCA. Firstly, learning mechanism can adjust the expertness of member search engine in a certain domain by analyzing users behavior. Only when the expertness of member search engine reaches a certain value, can the member search engine be employed by meta search engine. Above all, we employ FCA to merge all the search results on the assumption that if a web page is retrieved by more member search engines it is more important. In the concept lattice, the intent of the concept includes member search engines and the extent of the concept includes the web pages retrieved by those member search engines. Because of its hierarchy structure, we can rank concepts by the number of its intents, and then rank the web pages included by the same concept according to their original places in member search engines and the expertness of member search engine which retrieved them.
Qinhua Dong Yajun Du Fugui Wang
School of Mathematics & Computer Engineering, Xihua University, Chengdu, Sichuan, 610039, China
国际会议
2008 International Conference on Audio，Language and Image Processing(2008国际声音、语言、图像过程大会)
镇江
英文
624-628
2008-07-07（万方平台首次上网日期，不代表论文的发表时间）