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Digitizing strategy on the same ontology in heterogeneous data source

机译:异构数据源中相同本体的数字化策略

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The existence of the data objects, having the same ontology in heterogeneous data source(SO-HDS), is always the difficulty in cleaning process. Nowadays, there are several matching algorithms which can detect these data, such as Descartes Method, Enhanced Descartes Method and Priority Queue Algorithm. All these algorithms detect the similarity among the data directly without any pre-process on the original data. In this paper, we put forward a digitizing strategy on matching data objects based on the ontology of data object. When the data objects have the feature of SO-HDS, the storage mode and expression of these data objects can be ignored. We also propose a new data matching algorithm to find out the data objects having SO-HDS with the help of physics store attribute of data object. The new digitizing strategy will reduce the comparison amongst data objects, and keep the accuracy at the same time.
机译:在异构数据源(SO-HDS)中具有相同本体的数据对象的存在始终是清理过程中的难题。如今,有几种可以检测到这些数据的匹配算法,例如笛卡尔方法,增强笛卡尔方法和优先级队列算法。所有这些算法都可以直接检测数据之间的相似性,而无需对原始数据进行任何预处理。本文提出了一种基于数据对象本体的匹配数据对象的数字化策略。当数据对象具有SO-HDS功能时,可以忽略这些数据对象的存储模式和表达。我们还提出了一种新的数据匹配算法,借助数据对象的物理存储属性来找出具有SO-HDS的数据对象。新的数字化策略将减少数据对象之间的比较,并同时保持准确性。

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