Journal of Computer Applications ›› 2012, Vol. 32 ›› Issue (04): 1094-1096.DOI: 10.3724/SP.J.1087.2012.01094

• Database technology • Previous Articles     Next Articles

Ontology similarity computation using k-partite ranking method

LAN Mei-hui1,REN You-jun1,XU Jian1,GAO Wei2,3   

  1. 1. College of Computer Science and Engineering, Qujing Normal University, Qujing Yunnan 655011, China
    2. College of Information, Yunnan Normal University , Kunming Yunan 650092, China
    3. College of mathematical Sciences, Soochow University, Suzhou Jiangsu 215006, China
  • Received:2011-10-31 Revised:2011-12-06 Online:2012-04-20 Published:2012-04-01
  • Contact: LAN Mei-hui
  • Supported by:
    the National Natural Science Foundation Project of China

k-部排序本体相似度计算

兰美辉1,任友俊1,徐坚1,高炜2,3   

  1. 1. 曲靖师范学院 计算机科学与工程学院,云南 曲靖 655011
    2. 苏州大学 数学科学学院, 江苏 苏州 215006
    3. 云南师范大学 信息学院, 昆明 650092
  • 通讯作者: 兰美辉
  • 作者简介:兰美辉(1982-),女,云南宜良人,讲师,硕士,主要研究方向:信息检索、机器学习;任友俊(1973-),男,云南曲靖人,副教授,博士,主要研究方向:计算机网络;徐坚(1977-),男,云南曲靖人,副教授,主要研究方向:对等网络;高炜(1981-),男,浙江绍兴人,讲师,博士,主要研究方向:统计学习理论。
  • 基金资助:
    国家自然科学基金项目;香港研究基金委员会

Abstract: This paper represented the information of each vertex in ontology graph as a vector. According to its structure of ontology graph, the vertices were divided into k parts. It chose vertices from each part, and chose the ranking loss function. It used k-partite ranking learning algorithm to get the optimization ranking function, mapped each vertex of ontology structure graph into a real number, and then calculated the relative similarities of concepts by comparing the difference between real numbers. The experimental results show that the method for calculating the relative similarity between the concepts of ontology is effective.

Key words: ontology, similarity computation, k-partite ranking, ranking function, ranking loss function

摘要: 将本体图中每个顶点的相关信息用一个向量表示。根据本体图自身的结构将顶点分成k个部分。在每个部分中选取样本点组成S,并选择相应的排序亏损函数。运用k-部排序学习算法得到最优排序函数,从而将本体结构图中每个顶点映射成一个实数,通过比较实数间的差值判断两概念的相似程度。实验表明该方法对于计算本体概念间的相对相似度是有效的。

关键词: 本体, 相似度计算, k-部排序, 排序函数, 排序亏损函数

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