Abstract
Web search engines have become extremely popular in providing requested information to the user. The result set effectiveness of Web search engines has been continuously improving over the years. However, the documents of the result set may also contain irrelevant information having no importance to the user. So, the user has to spend some effort in searching for relevant information in these result set documents. To overcome this searching overhead, Web object search engines have been proposed. Such systems are built by extracting object information from various Web documents and integrating them into object repository. The user is provided with the facility to submit object search queries and the required object information is retrieved. Unlike, Web search engines, providing results to geography-specific queries is still in nascent stage for Web object search engines. Recently, Gaussian Mixture Model based technique for geographical labeling of Web objects was proposed in the literature. However, there is significant scope to improve the labeling accuracy results obtained in this technique. In this chapter, maximum marginal classifier-based technique for Web object geographical labeling is proposed. The advantages of this proposed technique are empirically exhibited on a real-world data set. This proposed technique, outperforms the contemporary technique by at least 40% in labeling accuracy, and is twice better in execution efficiency.
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Anjan Kumar, K.N., Satish Kumar, T., Reshma, J. (2021). Geographical Labeling of Web Objects Through Maximum Marginal Classification. In: Stahlbock, R., Weiss, G.M., Abou-Nasr, M., Yang, CY., Arabnia, H.R., Deligiannidis, L. (eds) Advances in Data Science and Information Engineering. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-71704-9_52
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