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首页> 外文期刊>Journal of ambient intelligence and humanized computing >Research on a new automatic generation algorithm of concept map based on text analysis and association rules mining
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Research on a new automatic generation algorithm of concept map based on text analysis and association rules mining

机译：基于文本分析和关联规则挖掘的概念图自动生成算法研究

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摘要

As an important knowledge visualization tool, concept map has become a research hotspot in educational data mining. Traditional concept map generation algorithms are difficult to generate concept maps quickly because of their strong reliance on experts' experience. A hybrid TA-ARM algorithm for automatic generation of concept map based on text analysis and association rule mining is proposed. The TA-ARM algorithm fully considers the association rules between concepts, uses the text classification algorithm in text analysis technology instead of manually classify the questions into concepts, and combines the association rule mining method to generate concept maps. The experimental result shows that the TA-ARM algorithm can automatically and rapidly generate the concept map, which not only reduces the impact of outside experts, but can also dynamically adjusts the concept map based on the parameters such as the threshold of confidence between test questions. The concept map generated by the TA-ARM algorithm expresses the association rules between the concepts and the degree of closeness through the associated pairs and relevant degree, and can clearly show the structural associations between concepts. The contrast experiment shows that the quality of the concept map automatically generated by the TA-ARM has a high quality and can visualize the associations between concepts and provide optimization and guidance for knowledge visualization.
机译：概念图作为重要的知识可视化工具，已成为教育数据挖掘的研究热点。传统的概念图生成算法因其严重依赖专家的经验而难以快速生成概念图。提出了一种基于文本分析和关联规则挖掘的混合TA-ARM概念图自动生成算法。 TA-ARM算法充分考虑概念之间的关联规则，在文本分析技术中使用文本分类算法，而不是将问题手动分类为概念，并结合关联规则挖掘方法以生成概念图。实验结果表明，TA-ARM算法可以自动，快速地生成概念图，不仅减少了外部专家的影响，还可以根据测试题之间的置信度阈值等参数动态调整概念图。 。 TA-ARM算法生成的概念图通过关联对和相关程度表达概念与紧密度之间的关联规则，可以清晰地显示概念之间的结构关联。对比实验表明，TA-ARM自动生成的概念图的质量较高，可以可视化概念之间的关联，并为知识可视化提供优化和指导。

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