利用相互增强关系迭代计算本体中概念与关系的重要性  

Ranking by Mutually Reinforcing Concepts and Relations in Ontology

作　　者：吴刚[1] 张阔[1] 李涓子[1] 王克宏[1] 

机构地区：[1]清华大学计算机科学与技术系知识工程实验室,北京100084

出　　处：《计算机学报》2007年第9期1490-1499,共10页Chinese Journal of Computers

基　　金：国家自然科学基金(90604025)资助

摘　　要：通过排序本体中概念重要性和关系权重的方式评价本体,能够辅助领域专家改进本体设计,辅助语义Web搜索引擎实现.现有链接分析技术不能直接应用于对概念的排序,而且缺乏有效方法对关系赋予权重.文中提出依据本体的图结构特点,以Hub值代替Authority值作为概念重要性,并利用本体中概念和关系相互增强的迭代方式计算概念重要性和关系权重.证明该迭代过程收敛于迭代方程组的不动点.实验初步表明,该方法具有与PageRank接近的收敛速度,并能得到合理的概念重要性与关系权重的排序结果.　Ranking the importance of concepts and the weights of relations is an effective method for evaluating an ontology, which can improve the design of ontology for domain expert, and be used as a component of semantic Web search engine. Current link analysis ranking algorithms cannot be directly applied to rank concepts, or efficiently to assign weights to relations. According to the characteristic of ontology graph structure, an algorithm is proposed with Hub rating instead of Authority rating as importance of concepts. The algorithm mutually reinforces importance of concepts and weights of relations in the iteration process which is proved to converge to the fixpoint of equations. The experimental results show the algorithm has the similar convergence speed to PageRank but more reasonable ranking of concepts importance and relations weights.

关 键 词：本体 语义WEB 排序 链接分析 收敛 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]