论文标题
基于知识图的记录链接方法
Knowledge graph based methods for record linkage
论文作者
论文摘要
如今，在历史人口统计学中很常见的是，由于生活方式的主要方法是理解人口统计学行为，家庭过渡，流动性等，因此使用个体级别数据的使用是这些学科的关键，因为它允许增加体积和数据复杂性。但是，当前的方法被限制为链接来自同类来源的数据。知识图是灵活的语义表示，可以以结构化的方式编码数据可变性和语义关系。 在本文中，我们提出了知识图来处理记录链接任务。所提出的方法，名为{\ bf werl}，利用了主要的知识图属性，并学习嵌入向量以编码人口普查信息。这些嵌入适当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此方法，并将其与刺激和令人满意的结果的相关方法进行了比较。
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. Record linkage advance is key in these disciplines since it allows to increase the volume and the data complexity to be analyzed. However, current methods are constrained to link data coming 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 knowledge graph use to tackle record linkage task. The proposed method, named {\bf 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.
