  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.
