Reusing Alignments for Discovering Instances Correspondences

Wafa Ghemmaz, Fouzia Benchikha and Maroua Bouzid
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Wafa Ghemmaz: LIRE Laboratory, Abdelhamid Mehri Constantine 2 University, Algeria
Fouzia Benchikha: LIRE Laboratory, Abdelhamid Mehri Constantine 2 University, Algeria
Maroua Bouzid: GREYC Laboratory, University of Caen Normandie, France

International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 2021, vol. 16, issue 4, 60-95

Abstract: Recently, instance matching has become a key technology to achieve interoperability over datasets, especially in linked data. Due the rapid growth of published datasets, it attracts increasingly more research interest. In this context, several approaches have been proposed. However, they do not perform well since the problem of matching instances that possess different descriptions is not addressed. On the other hand, the usage of the identity link owl:sameAs is generally predominant in linking correspondences. Unfortunately, many existing identity links are misused. In this paper, the authors discuss these issues and propose an original instance matching approach aiming to match instances that hold diverse descriptions. Furthermore, a novel link named ViewSameAs is proposed. The key improvement compared to existing approaches is alignment reuse. Thus, two novel methods are introduced: ViewSameAs-based clustering and alignment reuse based on metadata. Experiments on datasets by considering those of OAEI show that the proposed approach achieves satisfying and highly accuracy results.

Date: 2021
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