Scalable Matching and Clustering of Entities with FAMER

A. Saeedi, Markus Nentwig, E. Peukert, E. Rahm
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引用次数: 34

Abstract

Entity resolution identifies semantically equivalent entities, e.g. describing the same product or customer. It is especially challenging for Big Data applications where large volumes of data from many sources have to be matched and integrated. We therefore introduce a scalable entity resolution framework called FAMER (FAst Multi-source Entity Resolution system) that is based on Apache Flink for distributed execution and that can holistically match entities from multiple sources. For the latter purpose, FAMER includes multiple clustering schemes that group matching entities from different sources within clusters. In addition to previously known clustering schemes FAMER includes new approaches tailored to multi-source entity resolution. We perform a detailed comparative evaluation of eight clustering schemes for different real-life and synthetically generated datasets. The evaluation considers both the match quality as well as the scalability for different numbers of machines and data sizes.
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基于FAMER的实体可伸缩匹配与聚类
实体解析识别语义上等价的实体,例如描述相同的产品或客户。对于需要匹配和集成来自多个来源的大量数据的大数据应用来说,这尤其具有挑战性。因此,我们引入了一个可扩展的实体解析框架,称为FAMER(快速多源实体解析系统),它基于Apache Flink进行分布式执行,可以整体匹配来自多个来源的实体。对于后一种目的,FAMER包括多个聚类方案,将来自不同来源的匹配实体分组在集群中。除了先前已知的聚类方案外,FAMER还包括针对多源实体解析量身定制的新方法。我们对不同的真实数据集和合成数据集的八种聚类方案进行了详细的比较评估。评估既考虑匹配质量,也考虑不同机器数量和数据大小的可伸缩性。
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