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1. 云南师范大学信息学院
2. 云南师范大学 民族教育信息化教育部重点实验室
Published:2013
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高炜, 梁立, 徐天伟, et al. Semi-Supervised k-Partite Ranking Algorithm and Application in Ontology[J]. 2013, 34(2): 140-146.
为使k-部排序学习算法对所有结构的本体图都有效
提出一种半监督k-部排序学习算法.将训练样本集分成带标记和不带标记两类
通过推进的方法优化指数亏损模型
得到组合权值;并通过贪心的方法得到排序特征
由此得到排序函数.从而将本体图(或多本体结构图)中每个顶点映射成一个实数
由本体顶点对应实数间的差值判断概念间的相似程度.通过两个实验说明了新算法对于本体相似度计算和在不同本体间建立映射是有效的.
It presented a ranking algorithm called semi-supervised k-partite ranking algorithm in order to adapt all the structure of ontology graphs.The sample set was consisted of labeled and unlabeled data.It obtained the combination weights by minimum exponential loss use boosting based algorithm
and got ranking features in a greedy manner.Thus
ranking function was given which mapping each vertex in ontology graph(or
multi-ontology graph) to a real number
and the similarity was measured by considering the difference between two real numbers.Experiment shows that new algorithms for ontology similarity measuring and building ontology maps among different ontologies are efficient.
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