Ontology-based Information Integration In The Application Of Teaching Resources

With the development of the Internet, a growing number of heterogeneous data sources appear in the teaching resources. In order to make full use of these teaching resources, people need to solve these heterogeneous data sources integration problems urgently. In this paper, the introduction of ontology is to achieve heterogeneous data information integration in the teaching resources. The introduction of ontology is conductive to resolving semantic heterogeneous issue of information integration.Based on extensive applications of relational databases, heterogeneous data sources major consideration is relational database in the paper. Relational database cover a lot of rich information, and most construction of them have the participation of experts in the field, from which extract ontology automatically will acquier more useful semantic information.Based on the analysis of information integration technology which is based-on ontology (single ontology method, multiple ontology method and hybrid method), We present the teaching resources information integration system model based-on ontology which is on the basis of reference of hybrid method, give the flow chart of the information integration system too. By a detailed analysis of the flow chart, we find that the method of extracting ontology from the relational database and integration of ontologies to achieve information integration in teaching resources is conductive to adding new sources and the reuse of existed ontology, so as to solve the problems which it is difficult to add new sources in the multi-ontology method and reuse the existed ontology in the hybrid method.This paper presents a new method of extracting ontology from relational database. First designing a relational database schema extraction to realize the extraction of different relational database schema, Then, proposing conversion rules which is used to acquier the concepts, the contacts and justices of ontologies in acc- ordance with the different relational schemas, Last, giving a conversion algorithm from relational schema to OWL ontology and giving an examaple to show it.This paper mainly adopts the integration of different ontologies to achieve the integration of heterogeneous data sources. It presents a more comprehensive semantic similarity algorithm of different concepts to calculate similarity. Chromatography clustering algorithm is applied to integration of concepts in the ontologies. Finding the similar concepts to analyze the clustering results. At last, We discuss two disposing methods of clustering results, and give an example to prove it.