Knowledge graph based methods for record linkage

TL;DR

This paper introduces WERL, a novel knowledge graph-based method that uses learned embeddings to improve record linkage in historical demographic data, addressing limitations of traditional methods.

Contribution

It proposes a new approach leveraging knowledge graphs and embedding techniques for more flexible and effective record linkage across diverse data sources.

Findings

WERL outperforms existing methods on benchmark datasets.

Knowledge graph embeddings enhance record linkage accuracy.

The method effectively encodes semantic relations in demographic data.

Abstract

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.