Knowledge graph based methods for record linkage
This work addresses the need for linking diverse data sources in historical demography, offering a flexible approach that is incremental over existing methods.
The paper tackles the record linkage problem in historical demography by proposing a knowledge graph-based method called WERL, which learns and weights embeddings to encode census information, achieving stimulating and satisfactory results on benchmark datasets.
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.