一种基于子图近似同构的e-Learning学习资源本体匹配方法

e-Learning resource ontology matching algorithm based on subgraph approximate isomorphic

针对e-Learning学习资源本体异构问题,提出一种基于子图近似同构的本体匹配方法。该方法对现有本体匹配方法进行扩展,综合编辑距离、层次关系等特征,计算本体的结构级相似性,以点、边有序交替匹配来判断实体的有向图近似同构问题,实现本体匹配判定。演示算法处理过程,给出算法时间复杂度理论分析,说明其有效性。

Abstract： For the heterogeneity problem among ontologies, this paper presented an algorithm based on sub-graph approximate isomorphic here. It was an extension of existing methods in ontology matching. Under the comprehensive application of features such as edit distance and hierarchical relations, it calculated the similarity of graph structures between two ontologies. Lastly, it determined the ontology matching on the condition of sub-graph approximate isomorphism based on the alternately mapping of nodes and arcs in the describing graphs of ontologies. It used an example to demonstrate this ontology matching process and analyzed the time complexity to explain its effectiveness.