IMPROVED FOCUSED CRAWLING USING BAYESIAN OBJECT BASED APPROACH | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 4, Volume 18, Issue 1, January 2008, Page 49-60 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2008.65223 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Ahmed Ghozia1; Hoda Sorour2; Ashraf Aboshosh3 | ||||
| 1Dept. of computer Science and Eng., Faculty of Electronic Engineering, Menoufya University, EGYPT | ||||
| 2Dept. of computer Science and Eng., Faculty of Electronic Engineering, Menoufya University, EGYPT. | ||||
| 3Eng. Dept., NCRRT, Atomic Energy Authority, EGYPT | ||||
| Abstract | ||||
| The rapid growth of the World-Wide-Web made it difficult for general purpose search engines, e.g. Google and Yahoo, to retrieve most of the relevant results in response to the user queries. A vertical search engine specialized in a specific topic became vital. Building vertical search engines is accomplished by the help of a focused crawler. A focused crawler traverses the web selecting out relevant pages to a predefined topic and neglecting those out of concern. The focused crawler is guided toward those relevant pages through a crawling strategy. In this paper, a new crawling strategy is presented that helps building a vertical search engine. With this strategy, the crawler is kept focused to the user interests toward the topic. We build a model that describes the Web pages' features that distinguish relevant Web documents from those that are irrelevant. This is accomplished in the form of a supervised learning process, the web page is treated as an object having a set of features, and the features' values determine the relevancy of the web page through a Bayesian model. Results from practical experiments proved the efficiency of the proposed crawling strategy. | ||||
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