Ontology alignment is a fundamental task to support information sharing and reuse in heterogeneous information systems. Optimizing the combination of matchers by evolutionary algorithms to align ontology is an effective method. However, such methods have two significant shortcomings: weights need to be set manually to combine matchers, and a reference alignment is required during the optimization process. In this paper, a meta-matching approach GSOOM for automatically configuring weights and threshold using grasshopper optimization algorithm (GOA) has been proposed. In this approach, the ontology alignment problem is modeled as optimizing individual fitness of GOA. A fitness function is proposed, which includes two goals: maximizing the number of matching and the similarity score. Since it does not require an expert to provide a reference alignment, it is more suitable for real-world scenarios. To demonstrate the advantages of the approach, we conduct exhaustive experiments tasks on several standard datasets and compare its performance to other state-of-the-art methods. The experimental results illustrate that our approach is more efficiently and is significantly superior to other metaheuristic-based methods.

本体对齐是支持异构信息系统中信息共享和重用的一项基本任务。通过进化算法优化匹配器的组合以对齐本体是一种有效的方法。但是，这样的方法有两个明显的缺点：需要手动设置权重以组合匹配器，并且在优化过程中需要参考对齐。本文提出了一种利用蚱hopper优化算法（GOA）自动配置权重和阈值的元匹配方法GSOOM。在这种方法中，将本体对齐问题建模为优化GOA的个体适应性。提出了一个适应度函数，它包括两个目标：最大化匹配数和相似性分数。由于不需要专家来提供参考对齐，因此它更适合于实际情况。为了证明该方法的优势，我们在几个标准数据集上进行了详尽的实验任务，并将其性能与其他最新方法进行了比较。实验结果表明，我们的方法更加有效，并且明显优于其他基于元启发式的方法。