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Erschienen in:
2012 | OriginalPaper | Buchkapitel
A SVM-Based Text Classification System for Knowledge Organization Method of Crop Cultivation
verfasst von : Laiqing Ji, Xinrong Cheng, Li Kang, Daoliang Li, Daiyi Li, Kaiyi Wang, Yingyi Chen
Erschienen in: Computer and Computing Technologies in Agriculture V
Verlag: Springer Berlin Heidelberg
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The organization of crop cultivation practices is still far from completion, and Web Resources are not used adequately. This paper proposed a method, based on SVM, to organize the knowledge of crop cultivation practices efficiently from Web Resources. The knowledge organization method of crop cultivation was proposed with Good Agricultural Practices (GAP) in the application of the crop cultivation practices. It is that how to organize the existing crop cultivation knowledge, according to the requirements of crop cultivation practices. It mainly includes a text classification method and a search strategy on the knowledge of crop cultivation. For the text classification method, it used a text classification method based on SVM Decision Tree; for the search strategy, it used a strategy, organized by Ontology and custom knowledge bases. The experiment shows that performance of the proposed text classification method and the knowledge organization method with wheat, is workable and feasible.