Hybrid large-scale ontology matching strategy on big data environment

Ontology matching is one of the essential methodologies to overcome heterogeneity issues. Multiple knowledge-based and information systems perform ontology matching strategies to find correspondences between several ontologies for the purpose of discovering valuable information across various domains. The design and implementation of matching systems raises several challenges, especially, the matching accuracy and the performance issues. Accordingly, adapting the system to the requirements of Big Data era brings additional perspectives and challenges. Furthermore, to provide on-the-fly matching and in-time processing, the system must handle matching accuracy, runtime complexity and performance issues as an entire matching strategy. To this end, this paper presents a new hybrid ontology matching approach that benefit on one hand from the opportunities offered by parallel platforms, and on the other hand from ontology matching techniques, while applying a resource-based decomposition to improve the performance of the system.