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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ghozia, A. Sorour, H. Aboshosha, A. |
| Copyright Year | 2008 |
| Description | Author affiliation: Comput. Eng. & Sci. Dept., Menofiya Univ., Menouf (Ghozia, A.; Sorour, H.) |
| 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. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 1420802 |
| Page Count | 8 |
| File Format | |
| ISBN | 9789775031952 |
| DOI | 10.1109/NRSC.2008.4542363 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-03-18 |
| Publisher Place | Egypt |
| Access Restriction | Subscribed |
| Rights Holder | Egypt Acad. of Sci. Res. |
| Subject Keyword | Bayesian methods Web pages Crawlers Search engines Uniform resource locators Supervised learning Indexing Global Positioning System Predictive models Genetic algorithms |
| Content Type | Text |
| Resource Type | Article |
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