Semantic Ontology Method of Learning Resource based on the Approximate Subgraph Isomorphism

Abstract

Digital learning resource ontology is often based on different specification building. It is hard to find resources by linguistic ontology matching method. The existing structural matching method fails to solve the problem of calculation of structural similarity well. For the heterogeneity problem among learning resource ontology, an algorithm is presented based on subgraph approximate isomorphism. First of all, we can preprocess the resource of clustering algorithm through the semantic analysis, then describe the ontology by the directed graph and calculate the similarity, and finally judge the semantic relations through calculating and analyzing different resource between the ontology of different learning resource to achieve semantic compatibility or mapping of ontology. This method is an extension of existing methods in ontology matching. Under the comprehensive application of features such as edit distance and hierarchical relations, the similarity of graph structures between two ontologies is calculated. And, the ontology matching is determined on the condition of subgraph approximate isomorphism based on the alternately mapping of nodes and arcs in the describing graphs of ontologies. An example is used to demonstrate this ontology matching process and the time complexity is analyzed to explain its effectiveness