An Effective Approach for Large Ontology Matching Using Multi-objective Grasshopper Algorithm

Although the population-based metaheuristic ontology matching approaches have achieved excellent results on small scale matching tasks, such methods do not solve the large ontology matching problem. In addition, the common practice of the ontology matching community is to divide the large ontology into many small fragments. Although the divide-and-conquer strategy is feasible to reduce time and space complexity, it can easily change the original structure of the ontology, resulting in reduced quality. Further, if the produced fragments are smaller, the number of fragments will increase, which introduces new time and space complexity. In this paper, an effective approach for large ontology matching using multi-objective metaheuristic Grasshopper algorithm is proposed, called GOLOM. In this approach, an ontology pruning technique is proposed to reduce time and space complexity while maintaining the original structure. An effective background knowledge is built to assist the basic matcher in a proper way. In order to demonstrate the performance of GOLOM, three large ontology matching tasks were conducted. Experimental results show that GOLOM significantly reduces time and memory complexity compared to partition-based. In terms of alignment quality, GOLOM outperforms all the state-of-the-art systems.