Element matching by concatenating linguistic-based matchers and constraint-based matcher

Jingtao Zhou, Shusheng Zhang, Mingwei Wang, Han Zhao, Chao Zhang, Peng Li, Xiaofeng Dong, Kefei Wang
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引用次数: 7

Abstract

Although a lot of previous work on schema matching has developed many partial automatic matches for specific application domains, combining multiple match techniques enables achieving high accuracy for a large variety of match circumstances. In this context, we present a schema-based element matching approach that concatenates linguistic-based matchers and a constraint-based matcher. We propose a basic processing of our element level match approach in terms of a sequence of linguistic-based match and constraint-based match. We also provide a composite element name matcher to automatically combine linguistic-based match algorithms with a maximum priority strategy, and a neural network matcher to categorize elements of schemas by using element constraints with results from composite name matcher for joint consideration of multiple criteria. The concatenation of composite name matcher and neural network matcher enable our approach to adapt to more complex matching circumstance
通过连接基于语言的匹配器和基于约束的匹配器来进行元素匹配
尽管之前关于模式匹配的许多工作已经为特定的应用领域开发了许多部分自动匹配,但是结合多种匹配技术可以在各种匹配情况下实现高精度。在这种情况下,我们提出了一种基于模式的元素匹配方法,该方法将基于语言的匹配器和基于约束的匹配器连接在一起。根据基于语言的匹配和基于约束的匹配的序列,我们提出了元素级匹配方法的基本处理。我们还提供了一个复合元素名称匹配器,用于自动结合基于语言的匹配算法和最大优先级策略,以及一个神经网络匹配器,通过使用元素约束和复合名称匹配器的结果对模式的元素进行分类,以联合考虑多个标准。复合名称匹配器和神经网络匹配器的结合使我们的方法能够适应更复杂的匹配环境
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