A Parallel Approach for Matching Large-scale Ontologies

Abstract

Recent years have seen an increasing use of large ontologies in various areas of knowledge, e.g. health and agriculture. In this scenario, ontology matching is an important way of establishing interoperability between applications that use different but related ontologies. Matching large ontologies is challenging since it involves a great number of comparisons between concepts which leads to high execution time and requires a large amount of computing resources. This work proposes a MapReduce-based approach that distributes the computation of comparisons between concepts among the nodes of a cloud computing infrastructure. Experimental results indicate that the proposed approach can
reduce execution time without necessarily compromising the effectiveness of ontology alignments.