Abstract
The exponential growth of resources available in the Web has made it important to develop instruments to perform search efficiently. This paper proposes an approach for chemical information discovery by using focused crawling. The comparison of combination using various feature representations and classifier algorithms to implement focused crawlers was carried out. Latent Semantic Indexing (LSI) and Mutual Information (MI) were used to extract features from documents, while Naive Bayes (NB) and Support Vector Machines (SVM) were the selected algorithms to compute content relevance score. It was found that the combination of LSI and SVM provided the best solution.
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© 2007 Springer-Verlag Berlin Heidelberg
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Xia, Z., Guo, L., Liang, C., Li, X., Yang, Z. (2007). Focused Crawling for Retrieving Chemical Information. In: Corchado, E., Corchado, J.M., Abraham, A. (eds) Innovations in Hybrid Intelligent Systems. Advances in Soft Computing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74972-1_56
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DOI: https://doi.org/10.1007/978-3-540-74972-1_56
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