知识主题间先序关系挖掘

Mining prerequisite relations among learning objects

先序关系指知识主题之间学习的先后依赖关系。已有的先序关系挖掘方法大多是流线型的方式,易导致错误累计,且严重依赖可能导致错误先序关系的超链接。为了解决以上问题,先对知识主题间的先序关系进行统计分析,发现了先序关系的不对称性特征;接着提出从文本中挖掘知识主题间的先序关系的端到端先序关系挖掘模型。该模型基于文本中抽取出的术语间上下位关系,计算知识主题的相关术语集间先序关系的不对称性,进而预测知识主题间的先序关系。实验结果表明,该方法具有较优的先序关系抽取性能。

Prerequisite relation refers to the learning dependency between learning objects.Most previous works mined prerequisite relations in a pipelined way and heavily relied on hyperlinks,which lead to the accumulation of errors.To address these issues,prerequisite relations among knowledge topics were analyzed,and the asymmetry feature of prerequisite relation was found out.An end-to-end prerequisite relation model for mining prerequisite relations from texts was proposed.Based on the hyponymy relations between terms extracted from texts,this model calculates the asymmetry of prerequisite relation among related terms of learning objects,and then predicts the prerequisite relation betweens learning objects.The experimental results show that the proposed method achieves the state-of-the-art performance.