RUBIX:用于改进与关联数据的数据集成的框架

A. Assaf, E. Louw, A. Senart, Corentin Follenfant, Raphael Troncy, David Trastour
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引用次数: 7

摘要

由于今天的公共数据集包含数十亿个数据项,越来越多的公司希望将外部数据与传统企业数据集成,以改进商业智能分析。然而,这些分布式数据源表现出异构的数据格式和术语,并且可能包含噪声数据。在本文中,我们提出了RUBIX,这是一个新颖的框架,它使业务用户能够半自动地对可能有噪声的表格数据执行数据集成。这个框架为Google Refine提供了一个扩展,利用Freebase富类型提供了新颖的模式匹配算法。第一个实验表明,使用关联数据将单元格值与实例和带有类型的列标题进行映射,可以显著提高匹配结果的质量,因此应该会导致更明智的决策。
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RUBIX: a framework for improving data integration with linked data
With today's public data sets containing billions of data items, more and more companies are looking to integrate external data with their traditional enterprise data to improve business intelligence analysis. These distributed data sources however exhibit heterogeneous data formats and terminologies and may contain noisy data. In this paper, we present RUBIX, a novel framework that enables business users to semi-automatically perform data integration on potentially noisy tabular data. This framework offers an extension to Google Refine with novel schema matching algorithms leveraging Freebase rich types. First experiments show that using Linked Data to map cell values with instances and column headers with types improves significantly the quality of the matching results and therefore should lead to more informed decisions.
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