Nowadays, it is common in Historical Demography the use of individual-level data as a consequence of a predominant life-course approach for the understanding of the demographic behaviour, family transition, mobility, etc. Advanced record linkage is key since it allows increasing the data complexity and its volume to be analyzed. However, current methods are constrained to link data from the same kind of sources. Knowledge graph are flexible semantic representations, which allow to encode data variability and semantic relations in a structured manner.

In this paper we propose the use of knowledge graph methods to tackle record linkage tasks. The proposed method, named WERL, takes advantage of the main knowledge graph properties and learns embedding vectors to encode census information. These embeddings are properly weighted to maximize the record linkage performance. We have evaluated this method on benchmark data sets and we have compared it to related methods with stimulating and satisfactory results.

如今，在历史人口学中，由于主要的生活过程方法而导致使用个人级别的数据来理解人口统计学特征，家庭迁移，流动性等，这一点很普遍。高级记录链接是关键，因为它可以增加数据复杂性及其要分析的数量。但是，当前的方法被约束为链接来自相同种类源的数据。知识图是灵活的语义表示，它允许以结构化的方式对数据可变性和语义关系进行编码。

在本文中，我们建议使用知识图方法来解决记录链接任务。所提出的名为WERL的方法利用了主要知识图的特性，并学习了嵌入矢量来对普查信息进行编码。对这些嵌入进行适当的加权以最大化记录链接性能。我们已经在基准数据集上评估了该方法，并将其与相关方法进行了比较，并获得了令人满意的结果。