The growing usage of Semantic Web has resulted in an increasing number, size and heterogeneity of ontologies on the web. Therefore, the necessity of ontology matching techniques, which could solve these issues, is highly required. Due to high computational requirements, scalability is always a major concern in ontology matching system. In this work, a partition-based ontology matching system is proposed, which deals with parallel partitioning of the ontologies at multilevel. At first level, the root based ontology partitioning is proposed. Match able sub-ontology pair is generated using an efficient linguistic matcher (IEI-Sub) to uncover anchors and then based on maximum similarity values, pairs are generated. However, a distributed and parallel approach of Map Reduce-based IEI-sub process has been proposed to efficiently handle the anchor discovery process which is highly time-consuming. In second level partitioning, an efficient approach is proposed to form non-overlapping clusters. Extensive experimental evaluation is done by comparing existing approaches with the proposed approach, and the results shows that MPP-MLO turns out to be an efficient and scalable ontology matching system with 58.7% reduction in overall execution time.

中文翻译：

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MPP-MLO：有效匹配大型本体的多级并行分区

语义Web的使用不断增长，导致Web上本体的数量，规模和异构性都在增加。因此，迫切需要能够解决这些问题的本体匹配技术。由于高计算需求，可伸缩性一直是本体匹配系统中的主要关注点。在这项工作中，提出了一种基于分区的本体匹配系统，该系统在多层次上处理本体的并行分区。在第一级，提出了基于根的本体划分。使用高效的语言匹配器（IEI-Sub）生成可匹配的子本体对，以发现锚点，然后基于最大相似度值生成对。然而，已经提出了一种基于Map Reduce的IEI子过程的分布式并行方法，以有效地处理锚发现过程，这是非常耗时的。在第二级分区中，提出了一种有效的方法来形成不重叠的群集。通过将现有方法与提出的方法进行比较，进行了广泛的实验评估，结果表明，MPP-MLO是一种有效且可扩展的本体匹配系统，总体执行时间减少了58.7％。