Semantic focused crawler based on Q-learning and Bayes classifier

Semantic focused crawler is an important part of semantic vertical search engine. It is receiving increasing attention as a well founded alternative to search web with the problem of locating topical resource on entire web. In order to retrieval documents related to a given topic, in this paper, we propose QBLP Algorithm which enable crawler adaptive with the changing environment. This feature makes it possible to change behavior of focused crawler according to the particular environment and its relationships with the given input parameters during the search. QBLP Exploited Q learning which features whole-life learning and repayment delay accompany with Bayes classifier. It enables crawler to accumulate experience during the crawling and adjust strategy to achieve goal of making best decision under any circumstance. We make a comparison among QBLP, Best First and Breath First. According to statistics from experiments, We find that QBLP is superior on precision than others in long time crawling.