A Compact Flow Regime Algorithm for Improving the Efficiency of Metaheuristics-Based Ontology Matching System

Ontology matching is a key technology for solving the problem of semantic heterogeneity between ontologies. In past years, scholars have proposed many approaches committed to improve the quality of ontology matching. In particular, the metaheuristics-based ontology meta-matching approaches have been proven to be able to achieve better results because it can adaptively combine multiple basic matchers according to application scenarios. However, beside quality, the execution efficiency of the system is also of great importance in dynamic applications. If the system’s response time is too long, it will lead to a decline for user experience and even abandonment. To address such issues, in this paper, a novel compact flow regime algorithm for improving the efficiency of metaheuristics-based ontology matching system is proposed, named CFRAOM. The proposed approach uses the probability representation of population behavior to avoid the algorithm consuming a lot of time and memory during the execution of matching, thereby improving the execution efficiency of the matching system. To demonstrate the efficiency and matching effectiveness of the proposed approach. The CFRAOM algorithm has been executed several large-scale ontology matching tasks and compared with other metaheuristic-based methods. The experimental results indicate that the proposed approach not only improves the execution efficiency of the metaheuristic-based system but also achieves better matching quality.