Ontology matching by applying parallelization and distribution of matching task within clustering environment

Recent advances of information and communication technology provides huge amount of heterogeneous information available for us. But integration of information semantically and provide machine understandable meaning to information is still a great challenge in current web technology. To overcome the challenges, ontology matching plays a vital role, which is introduced by semantic web technology. In this paper, we propose a new method of ontology matching using parallelization and distribution technique. To apply parallelism, we develop a partitioning algorithm by using property-by-class and subclass of relationship, which partitions the ontology into smaller cluster. Then the clusters from different ontology are matched based on terminological and structural similarity with semantic verification. These entire tasks of matching are handled in a parallel way and all the tasks are distributed over the computational resources. Thus, we significantly reduce the time complexity and space complexity of large scale matching task. Our proposed method reduces misaligned pairs while increasing correct aligned concepts. Validity of our claims have been substantiated through different experiments on small and large ontologies.