A Framework for Deep Web Crawler Using Genetic Algorithm

نویسندگان

  • K. F. Bharati
  • Prof. P. Premchand
  • Prof. A. Govardhan
  • Dr. K. Anuradha
  • Dr. N. Sandhya
چکیده

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 multiagent 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. Keywords— Hidden Web Crawler, Reinforcement Learning, Multi-Agents, Web Mining, Information Retrieval.

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تاریخ انتشار 2013