专利和期刊隶属于不同的知识组织体系,要实现专利与期刊文献的交叉测览和检索必须解决两种分类法(中国图书馆分类法(CLC)和国际专利分类法(IPC))之间的映射问题.在调研现有分类法类目映射方法的基础上,讨论了基于机器学习实现中国图书馆分类法和国际专利分类法之间类目映射的方法.通过对中图法某个类目标识的语料进行训练得到该类目的分类器,然后用其对国际专利分类法标识的语料进行分类,对分类结果进行分析得出类目间的映射关系.对比实验证明了该方法的有效性.%Patents and journals belong to different knowledge organization systems. To achieve the cross-browsing and cross-retrieval between journal literature and patents, the mapping problem between two classifications Chinese Library Classification (CLC) and International Patent Classification (IPC), must be addressed. According to the survey of the existing methods of classification mapping, this paper discussed a method to achieve the mapping between CLC and IPC based on machine learning. The learner was got by training the corpus identified by the CLC category, with which to classify the corpus identified by the IPC category. The mapping relations can be found after analyzing the classification results. And the comparison experiment proves the effectiveness of this method.
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