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Crowd-assessing quality in uncertain data linking datasets
The Knowledge Engineering Review ( IF 2.1 ) Pub Date : 2020-07-02 , DOI: 10.1017/s0269888920000363
Daniel Faria , Alfio Ferrara , Ernesto Jiménez-ruiz , Stefano Montanelli , Catia Pesquita

The quality of a dataset used for evaluating data linking methods, techniques, and tools depends on the availability of a set of mappings, called reference alignment, that is known to be correct. In particular, it is crucial that mappings effectively represent relations between pairs of entities that are indeed similar due to the fact that they denote the same object. Since the reliability of mappings is decisive in order to perform a fair evaluation of automatic linking methods and tools, we call this property of mappings as mapping fairness. In this article, we propose a crowd-based approach, called Crowd Quality (CQ), for assessing the quality of data linking datasets by measuring the fairness of the mappings in the reference alignment. Moreover, we present a real experiment, where we evaluate two state-of-the-art data linking tools before and after the refinement of the reference alignment based on the CQ approach, in order to present the benefits deriving from the crowd assessment of mapping fairness.

中文翻译:

不确定数据链接数据集中的人群评估质量

用于评估数据链接方法、技术和工具的数据集的质量取决于一组映射的可用性,称为参考对齐,这被认为是正确的。特别是,映射有效地表示实际上相似的实体对之间的关​​系至关重要,因为它们表示相同的对象。由于映射的可靠性是对自动链接方法和工具进行公平评估的决定性因素,我们将映射的这种属性称为映射公平. 在本文中,我们提出了一种基于人群的方法,称为人群质量(重庆),用于通过测量参考对齐中映射的公平性来评估数据链接数据集的质量。此外,我们还展示了一个真实的实验,在此实验中,我们评估了两个最先进的数据链接工具,在参考对齐改进之前和之后基于重庆方法,以展示从人群评估中获得的利益映射公平性。
更新日期:2020-07-02
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