Dividing the Ontology Alignment Task

Large ontologies still pose serious challenges to state of the art ontology alignment systems. In the paper we present an approach that combines a lexical index, a neural embedding model and locality modules to effectively segment an input ontology matching task into smaller and more tractable (sub)matching tasks. We have conducted a comprehensive evaluation using the datasets of the Ontology Alignment Evaluation Initiative. The results are encouraging and suggest that the proposed methods are adequate in practice and can be integrated within the workflow of state of the art systems.大型本体仍对当前最先进的本体对齐（Ontology Alignment）系统构成严峻挑战。本文提出一种结合词汇索引、神经嵌入模型与局部性模块的方法，可将输入的本体匹配任务有效拆解为规模更小、更易于处理的（子）匹配任务。我们依托本体对齐评估倡议（Ontology Alignment Evaluation Initiative）的数据集开展了全面的评估实验，实验结果令人振奋，表明所提出的方法在实际应用中表现优异，且可集成至当前最先进的本体对齐系统的工作流程中。