Abstract
Focused crawler is the core of the focused search engine, and the POI-oriented user need is a kind of new focused object which has not been well solved in previous studies. In this paper, we design and realize a POI-oriented focused crawler. The proposed focused crawler adopts classifiers to make relevant judgment and considers both current page’s relevance and the URL link information to make the URLs’ priority judgment. Experiments were conducted with two kinds of classification algorithms of Naive Bayes (NB) and Support Vector Machines (SVMs) on four sites, respectively. Experimental results show that the focused crawler with NB classifier obtains the average harvest of 95.97%, higher than the one with SVMs by 45.53%, but the focused crawler with SVMs attains the higher recall.
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Hazman, M.: A survey of focused crawler approaches. J. Global Res. Comput. Sci. 3(4), 68–72 (2012)
Zhou, L., Lin, L.: Survey on the research of focused crawling technique. J. Comput. Appl. 25(9), 1965–1969 (2005)
Chakrabarti, S., van den Berg, M., Dom, B.: Focused crawling: a new approach to topic-specific Web resource discovery. In: Proceedings of the 8th International World Wide Web Conference, pp. 1623–1640. Elsevier Science, New York (1999)
Balaji, S., Sarumathi, S.: TOPCRAWL—community mining in web search engines with emphasize on topical crawling. In: Proceedings of the International Conference on Pattern Recognition, Informatics and Medical Engineering, Salem, Tamilnadu, 2012, pp. 20–24
Chen, H., Chung, Y.M., Marshall, R., Yang, C.C.: An intelligent personal spider(agent)for dynamic Internet searching. Decision Support Syst. 23(1), 41–58 (1998)
Liu, G., Kang, L., Luo, C.: Focused crawling strategy based on genetic algorithm. J. Comput. Appl. 27(12), 172–174 (2007)
Chen, Y., Zhang, Z., Zhang, T.: A searching strategy in topic crawler using ant colony algorithm. Microcomput. Appl. 30(1), 53–56 (2011)
Zheng, S.: Genetic and ant algorithms based focused crawler design. In: 2011 Second International Conference on Innovations in Bio-inspired Computing and Applications, Kaohsiung, Taiwan 2011
Chakrabarti, S., Dom, B., Indyk, P.: Enhanced hypertext categorization using hyperlinks. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, Washington, 1998, pp. 307–318
Johnson, J., Tsioutsiouliklis, K., Giles, C.L.: Evolving strategies for focused web crawling. In: Proceedings of the 20th International Conference on Machine Learning (ICML), Washington, 2003
Pant, G., Srinivasan, P.: Link contexts in classifier-guided topical crawlers. IEEE Trans. Knowl. Data Mining (2006)
Yuvarani, M., Iyengar, N.C.S.N., Kannan, A.: LSCrawler: a framework for an enhanced focused Web crawler based on link semantics. In: The 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI’06), Hong Kong, 2006
Jalilian, O., Khotanlou, H.: A new fuzzy-based method to weigh the related concepts in semantic focused web crawlers. In: 2011 3rd International Conference on Computer Research and Development (ICCRD), Shanghai, 2011, pp. 23–27
Peng, H., Wang, Y.: Real-time page classification oriented algorithm on topic extraction. Comput. Modern. 8–11 (2008)
Taylan, D., Poyraz, M., Akyokuş, S., Ganiz, M.C.: Intelligent focused crawler: learning which links to crawl. In: 2011 International Symposium on Innovations in Intelligent Systems and Applications (INISTA), Istanbul, 2011, pp. 504–508
Yuan, F.-y., Yin, C.-x., Liu, J.: Improvement of PageRank for focused crawler. In: SNPD 2007: 8th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007
Zhang, X., Li, Z., Hu, C.: Adaptive focused crawler based on tunneling and link analysis. In: 11th International Conference on Advanced Communication Technology, Gangwon-Do, 2009, pp. 2225–2230
Batsakis, S., Petrakis, E., Milios, E.: Improving the performance of focused web crawlers. Data Knowl. Eng. 68(10), 1001–1013 (2009)
Liu, P., Feng, J.: An improved Naive Bayes text categorization algorithm. Microcomput. Inform. 26(93), 187–188 (2010)
Tan, S.: Research on High-Performance Text Categorization. Institute of Computing Technology, Chinese Academy of Sciences, Beijing (2006)
Acknowledgments
This research is supported by project 61073119 under the National Natural Science Foundation of China and project BK2010547 under the Jiangsu Natural Science Foundation of China.
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Fan, X., Zhou, Js., Cheng, Cy., Zhou, Yc., Yin, D. (2013). Empirical Study of POI-Oriented Focused Crawler. In: Li, J., Qi, G., Zhao, D., Nejdl, W., Zheng, HT. (eds) Semantic Web and Web Science. Springer Proceedings in Complexity. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6880-6_25
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DOI: https://doi.org/10.1007/978-1-4614-6880-6_25
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