文章基本信息

标题：A Framework for Deep Web Crawler Using Genetic Algorithm
本地全文：下载
作者：K.F.Bharati ; Prof.P.Premchand ; Prof.A.Govardhan 等
期刊名称：International Journal of Electronics and Computer Science Engineering
电子版ISSN：2277-1956
出版年度：2013
卷号：2
期号：2
页码：602-609
出版社：Buldanshahr : IJECSE
摘要：Abstract-The Web has become one of the largest and most readily accessible repositories of human knowledge. The traditional search engines index only surface Web whose pages are easily found. The focus has now been moved to invisible Web or hidden Web, which consists of a large warehouse of useful data such as images, sounds, presentations and many other types of media. To use such data, there is a need for specialized technique to locate those sites as we do with search engines. This paper focuses on an effective design of a Hidden Web Crawler that can automatically discover pages from the Hidden Web by employing multi- agent Web mining system. A framework for deep web with genetic algorithm is used to discover the resource discovery problem and the results show the improvement in the crawling strategy and harvest rate.
关键词：Hidden Web Crawler; Reinforcement Learning; Multi-Agents; Web Mining; Information Retrieval.