A novel periodic learning ontology matching model based on interactive grasshopper optimization algorithm

摘要

The ontology matching is a significant task for data integration and semantic interoperability. Although a large number of effective ontology matching methods have been proposed in a fully automated way, user involvement during the matching process is needed for real-world applications. It has been recognized as an effective method for further improving the quality of matching, especially for very precise matching cases. However, involving users during complex matching process suffers from new challenges of how to reduce the burden on users and how to increase effective interaction. In this paper, we propose a novel periodic learning ontology matching model based on interactive grasshopper optimization algorithm to address the above-mentioned issues. This new model takes into account the periodic feedback from users during the optimization process, rather than every generation, and a roulette wheel method is introduced to select the most problematic candidate mappings to present to users, not all, and to reduce the burden on users. To ensure the effectiveness of the interaction, a reward and punishment mechanism is considered for candidate mappings to propagate the feedback of user, and to guide the search direction of the algorithm. The experiments, conducted on two interactive tracks from Ontology Alignment Evaluation Initiative (OAEI), show that the proposed model significantly improve the quality of matching. Compared to other state-of-the-art matching systems, our model outperforms other methods in almost all cases with given different error rate, which makes it one of the most advanced leaders. Finally, a typical case of data integration is studied to present how the proposed approach is able to help enterprises to harmonize product catalogs. (C) 2021 Elsevier B.V. All rights reserved.