基于本体的VSM在兴趣型学习社区分组中的应用  

Application of Ontology-based VSM on Learning Community Construction of Interest

作　　者：程艳[1,2] 许维胜[1] 赵斐[3] 何一文[1] 

机构地区：[1]同济大学电子与信息工程学院,上海201804 [2]江西师范大学计算机信息工程学院,江西南昌330022 [3]同济大学经济与管理学院,上海201804

出　　处：《同济大学学报（自然科学版）》2010年第5期736-743,共8页Journal of Tongji University:Natural Science

基　　金：国家自然科学基金资助项目(70871091;60804042)

摘　　要：采用语义网络技术,提出了基于本体的向量空间模型(VSM),计算学习者的兴趣向量,克服了传统的VSM有术语间语义相关性被忽略的不足,提高了兴趣相似性比较的精确程度,同时提出了一种基于学习者兴趣相似匹配度和学习者兴趣匹配浓度的学习社区自组织分组算法.针对模型使用本体中的概念构造向量空间表现出的巨大维数,运用概念索引降维法对兴趣特征矩阵进行合理降维,大大降低了计算的复杂性.最后通过应用案例验证分析了该模型算法具有较高的分组效率和良好的扩展性.To get rid of the disadvantages caused by neglecting the semantic relevance between terms in the traditional vector space model,ontology-based vector space model（VSM） based on semantic web technology is presented to calculate the learners’ interest eigenvector,which can enhance the relative accuracy of the interest similarity.And a self-organization grouping algorithm for community is put forward on the basis of the learners’ interest similarity match-degree and its concentration.Great dimensions would take place with the ontology to construct vector space,thus concept indexing（CI） method and reasonable treatment to matrix of interest Eigen value are used to promote the calculation efficiency.Finally,an experimental analysis of online education cases is carried out to verify the model algorithm with high efficiency and good scalability.

关 键 词：分组算法 本体 兴趣特征 向量空间模型 概念索引法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]