Scalability in ontology instance matching of large semantic knowledge base

Authors:
Masaki Aono;Md. Hanif Seddiqui
Affiliations:
Department of Information Sciences, Toyohashi University of Technology, Toyohashi, Japan;Dept. of Electronic and Computer Engineering, Toyohashi University of Technology, Toyohashi, Japan
Venue:
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Year:
2010

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Abstract

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.