An Efficient and Scalable Approach for Ontology Instance Matching

Abstract

Ontology instance matching is a key interoperability enabler across heterogeneous data resources in the Semantic Web for integrating data semantically. Although most of the research has been emphasized on schema level matching so far, research on ontology matching is shifting from ontology schema or concept level to instance level to fulfill the vision of “Web of Data”. Ontology instances define data semantically and are kept in knowledge base. Since, heterogeneous sources of massive ontology instances grow sharply day-by-day, scalability has become a major research concern in ontology instance matching of semantic knowledge bases. In this study, we propose a method by filtering instances of knowledge base into two stages to address the scalability issue. First stage groups the instances based on the relation of concepts and next stage further filters the instances based on the properties associated to instances. Then, our instance matcher works by comparing an instance within a classification group of one knowledge base against the instances of same sub-group of other knowledge base to achieve interoperability. We experiment our proposed method with several benchmark data sets namely OAEI-2009, OAEI-2010 and OAEI-2011. On comparison with other baseline methods, our proposed method shows satisfactory result.