  Whether the goal is to estimate the number of people that live in a
congressional district, to estimate the number of individuals that have died in
an armed conflict, or to disambiguate individual authors using bibliographic
data, all these applications have a common theme - integrating information from
multiple sources. Before such questions can be answered, databases must be
cleaned and integrated in a systematic and accurate way, commonly known as
record linkage, de-duplication, or entity resolution. In this article, we
review motivational applications and seminal papers that have led to the growth
of this area. Specifically, we review the foundational work that began in the
1940's and 50's that have led to modern probabilistic record linkage. We review
clustering approaches to entity resolution, semi- and fully supervised methods,
and canonicalization, which are being used throughout industry and academia in
applications such as human rights, official statistics, medicine, citation
networks, among others. Finally, we discuss current research topics of
practical importance.
