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Large-Scale Ontology Matching: State-of-the-Art Analysis

机译：大规模本体匹配：最新分析

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摘要

Ontologies have become a popular means of knowledge sharing and reuse. This has motivated the development of large-sized independent ontologies within the same or different domains with some overlapping information among them. To integrate such large ontologies, automatic matchers become an inevitable solution. However, the process of matching large ontologies has high space and time complexities. Therefore, for a tool to efficiently and accurately match these large ontologies within the limited computing resources, it must have techniques that can significantly reduce the high space and time complexities associated with the ontology matching process. This article provides a review of the state-of-the-art techniques being applied by ontology matching tools to achieve scalability and produce high-quality mappings when matching large ontologies. In addition, we provide a direct comparison of the techniques to gauge their effectiveness in achieving scalability. A review of the state-of-the-art ontology matching tools that employ each strategy is also provided. We also evaluate the state-of-the-art tools to gauge the progress they have made over the years in improving alignment's quality and reduction of execution time when matching large ontologies.
机译：本体已经成为知识共享和重用的流行手段。这激发了在相同或不同领域中具有相互重叠信息的大型独立本体的发展。为了集成如此大的本体，自动匹配器成为必然的解决方案。但是，匹配大型本体的过程具有很高的空间和时间复杂度。因此，对于一种在有限的计算资源内有效且准确地匹配这些大型本体的工具，它必须具有可以显着降低与本体匹配过程相关的高空间和时间复杂性的技术。本文概述了本体匹配工具正在应用的最新技术，以实现可扩展性并在匹配大型本体时产生高质量的映射。此外，我们直接比较了这些技术，以评估其在实现可伸缩性方面的有效性。还提供了对采用每种策略的最新本体匹配工具的评论。我们还评估了最先进的工具，以评估它们多年来在匹配大型本体时在提高对齐质量和减少执行时间方面取得的进展。

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