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首页 > 过刊浏览>2020年第43卷第7期 >111-120. DOI:10.11835/j.issn.1000-582X.2020.012
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面向维基百科的概念依赖关系挖掘方法
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中图分类号:

TP311

基金项目:

湖北省教育厅人文社科研究资助项目（19Q011）。


Concept dependency mining method for Wikipedia
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    摘要:

    在互联网技术高度发达的时代，网络上的学习资源呈现出指数型增长态势，面对各种学习对象、概念之间存在的多样化和无序性，如果能识别出之间的依赖关系，将有可能对计算机教育产生重要影响。针对该问题，提出一种面向维基百科的概念依赖关系识别方法，利用概念在维基百科中的特点，设计出一套识别概念依赖关系模型，在公共数据集上采用基于机器学习的分类算法进行测试。实验结果表明，该模型具有较高准确率和召回率，能够有效发现概念之间的依赖关系。

    Abstract:

    In the era of highly developed Internet technology, the learning resources on the Internet show an exponential growth trend. With the diversification and disorder between various learning objects and concepts, the recognition of the dependencies between them will have a major impact on computer education. Aiming at the solution to this problem, this paper proposed a concept dependency recognition method for Wikipedia. Using the characteristics of the concept in Wikipedia, a set of recognition concept dependency model was designed, and the machine learning based classification algorithm was used to test on the public data set. The experimental results show that with high accuracy and recall rate, the model can effectively discover the dependencies between concepts.

    参考文献
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周洋,肖奎,曾诚.面向维基百科的概念依赖关系挖掘方法[J].重庆大学学报,2020,43(7):111-120.

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  • 收稿日期:2020-01-18
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