Scalability in Ontology Instance Matching of Large Semantic Knowledge base
نویسندگان
چکیده
The rapid growth of heterogeneous sources of massive ontology instances raises a scalability issue in ontology instance matching of semantic knowledge bases. In this paper, we propose an efficient method of instance matching by considering secondary classification of monotonic large instances to achieve scalability. We use a taxonomy of the ACM’s Computing Classification System (CCS) for secondary classification of large set of instances from a version of DBLP and Rexa. Then we apply our ontology instance matching to achieve the interoperability in a faster and efficient way. The experiment and evaluation depict the effectiveness and scalability of our modified algorithm for ontology instance matching. Key–Words: Semantic Knowledge base, Ontology Instance Matching, Classification
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