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Interconnection of Heterogeneous Databases by Correlation Measurement based on Semantic Space Model

机译:基于语义空间模型的相关性度量异构数据库互连

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In this paper, we will introduce the new technology of correlation search a retrieval technique based on measurement space model. There are said to be two types of human's knowledge. One is the kind we obtain from reading literature. This may be called absolute knowledge. With ICT technology, it is knowledge that can be described by the ontology used in a conceptual dictionary and Semantic Web. Another kind may be called relative knowledge. This is para-digmatically, relational knowledge we gain when we compare knowledge A with knowledge B. Such knowledge may be connected in thought without being displayed (i.e, as a document on the Internet or being inscribed on a piece of paper). Humans often compare heterogeneous things. Moreover, humans are thought to unconsciously create the criteria for linking heterogeneous things. In this model, the criteria are represented by a set of axes which form space and by a measurement method which mathematically measures norms, distance or inner product. It is important to search not only for the correlation between heterogeneous knowledge, but also for factors (axis, index) that contribute to the correlation. This model will show correlations between heterogeneous knowledge and their factors, by deriving a selected knowledge cluster that has high correlation magnitude and a set of axes used to calculate the magnitude.
机译:在本文中,我们将介绍相关搜索的新技术,一种基于测量空间模型的检索技术。据说人类的知识有两种。一种是我们从阅读文学中获得的那种。这可以称为绝对知识。借助ICT技术,可以通过概念词典和语义网中使用的本体来描述知识。另一种可能称为相对知识。这是范式上的关系知识,是我们在将知识A与知识B进行比较时获得的。这种知识可能在思想上联系在一起而没有显示出来(例如,作为Internet上的文档或刻在纸上)。人类通常会比较各种事物。此外,人们认为人类会在不知不觉中创建链接异质事物的标准。在此模型中,标准由形成空间的一组轴和数学上测量规范,距离或内积的测量方法表示。重要的是,不仅要搜索异构知识之间的相关性,而且还要搜索有助于相关性的因素(轴,索引)。该模型将通过推导具有较高相关量级的选定知识集群和一组用于计算该量级的轴来显示异构知识及其因素之间的相关性。

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