Ontology mapping based on hidden Markov model  

基于隐马尔可夫模型的本体映射(英文)

作　　者：尹康银[1] 宋自林[1] 徐平 

机构地区：[1]解放军理工大学指挥自动化学院,南京210007 [2]海军电磁兼容研究检测中心,上海200235

出　　处：《Journal of Southeast University(English Edition)》2007年第3期389-393,共5页东南大学学报（英文版）

基　　金：The Weaponry Equipment Foundation of PLA Equipment Ministry (No51406020105JB8103)

摘　　要：The existing ontology mapping methods mainly consider the structure of the ontology and the mapping precision is lower to some extent. According to statistical theory, a method which is based on the hidden Markov model is presented to establish ontology mapping. This method considers concepts as models, and attributes, relations, hierarchies, siblings and rules of the concepts as the states of the HMM, respectively. The models corresponding to the concepts are built by virtue of learning many training instances. On the basis of the best state sequence that is decided by the Viterbi algorithm and corresponding to the instance, mapping between the concepts can be established by maximum likelihood estimation. Experimental results show that this method can improve the precision of heterogeneous ontology mapping effectively.当前本体映射方法主要考虑结构映射而且映射精度较低,根据统计理论思想,提出了一种基于隐马尔可夫模型的异构本体映射方法.该方法将概念表示为隐马尔可夫模型、概念的特性、关系、上下文、兄弟、规则等表示为隐马尔可夫模型的状态,通过对实例的学习建立隐马尔可夫模型.利用Viterbi算法确定实例所对应的状态序列,然后采用极大似然估计法确定该实例所对应的模型,从而建立异构本体之间的映射.实验表明,该方法有效地提高了异构本体映射的精度.

关 键 词：ontology heterogeneity ontology mapping hidden Markov model semantic web 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]