Knowledge Transfer for Entity Resolution with Siamese Neural Networks

M. Loster, Ioannis K. Koumarelas, Felix Naumann
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引用次数: 15

Abstract

The integration of multiple data sources is a common problem in a large variety of applications. Traditionally, handcrafted similarity measures are used to discover, merge, and integrate multiple representations of the same entity—duplicates—into a large homogeneous collection of data. Often, these similarity measures do not cope well with the heterogeneity of the underlying dataset. In addition, domain experts are needed to manually design and configure such measures, which is both time-consuming and requires extensive domain expertise. We propose a deep Siamese neural network, capable of learning a similarity measure that is tailored to the characteristics of a particular dataset. With the properties of deep learning methods, we are able to eliminate the manual feature engineering process and thus considerably reduce the effort required for model construction. In addition, we show that it is possible to transfer knowledge acquired during the deduplication of one dataset to another, and thus significantly reduce the amount of data required to train a similarity measure. We evaluated our method on multiple datasets and compare our approach to state-of-the-art deduplication methods. Our approach outperforms competitors by up to +26 percent F-measure, depending on task and dataset. In addition, we show that knowledge transfer is not only feasible, but in our experiments led to an improvement in F-measure of up to +4.7 percent.
基于Siamese神经网络的实体解析知识转移
在各种各样的应用程序中,多个数据源的集成是一个常见问题。传统上,手工制作的相似性度量用于发现、合并和集成相同实体的多个表示(副本)到大型同构数据集合中。通常,这些相似性度量不能很好地处理底层数据集的异质性。此外,需要领域专家手动设计和配置这些度量,这既耗时又需要广泛的领域专业知识。我们提出了一个深度连体神经网络,能够学习针对特定数据集特征定制的相似性度量。利用深度学习方法的特性,我们能够消除人工特征工程过程,从而大大减少模型构建所需的工作量。此外,我们还表明,可以将在一个数据集的重复数据删除过程中获得的知识转移到另一个数据集,从而显著减少训练相似性度量所需的数据量。我们在多个数据集上评估了我们的方法,并将我们的方法与最先进的重复数据删除方法进行了比较。根据任务和数据集的不同,我们的方法比竞争对手高出高达26%的F-measure。此外,我们表明,知识转移不仅是可行的,而且在我们的实验中导致F-measure的提高高达4.7%。
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