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.

本体匹配是数据集成和语义互操作性的重要任务。尽管已经以完全自动化的方式提出了大量有效的本体匹配方法，但在实际应用中需要用户参与匹配过程。它已被公认为进一步提高匹配质量的有效方法，特别是对于非常精确的匹配案例。然而，在复杂的匹配过程中涉及到用户，面临着如何减轻用户负担和如何增加有效交互的新挑战。在本文中，我们提出了一种基于交互式蚱蜢优化算法的新型周期性学习本体匹配模型来解决上述问题。这个新模型在优化过程中考虑了用户的周期性反馈，而不是每一代，并且引入轮盘赌的方法来选择最有问题的候选映射呈现给用户，而不是全部，并减少用户的负担。为了保证交互的有效性，候选映射考虑了奖励和惩罚机制来传播用户的反馈，并指导算法的搜索方向。在本体对齐评估倡议 (OAEI) 的两个交互式轨道上进行的实验表明，所提出的模型显着提高了匹配质量。与其他最先进的匹配系统相比，我们的模型在给定不同错误率的情况下几乎在所有情况下都优于其他方法，这使其成为最先进的领导者之一。最后，