Data Field Based Large Scale Ontology Mapping
Juan Li · Chinese Journal of Computers · 2010
As the cornerstone of ontology based data integration,data exchange and metadata management,ontology mapping,aiming to obtain semantic correspondences between two ontologies,has attracted wide attentions of researchers in community of the Semantic Web.However when getting the alignments between two large-scale ontologies,the existed mapping methods are not very effective and efficient due to neglecting of relevant sub-ontologies in those two ontologies.For addressing this important issue,in this paper,a data field based ontology mapping approach is proposed to improve the effectiveness and the efficiency of large scale ontology mapping tasks.At first,this approach employs a light weight similarity computing method to collect the initial relevance values between one ontology's elements to another ontology.Then,the potential functions of a data field are taken into account to revise the relevance of an ontology element according to its surroundings.Finally,the relevant sub-ontologies are found and extracted,and a fine-grained alignment approach is used to mapping between the extracted ontologies for better results.The experiments show that the proposed approach is able to effectively deal with the large scale ontology mapping issue with the satisfactory efficiency.