  In author name disambiguation, author forenames are used to decide which name
instances are disambiguated together and how much they are likely to refer to
the same author. Despite such a crucial role of forenames, their effect on the
performances of heuristic (string matching) and algorithmic disambiguation is
not well understood. This study assesses the contributions of forenames in
author name disambiguation using multiple labeled datasets under varying ratios
and lengths of full forenames, reflecting real-world scenarios in which an
author is represented by forename variants (synonym) and some authors share the
same forenames (homonym). Results show that increasing the ratios of full
forenames improves substantially the performances of both heuristic and
machine-learning-based disambiguation. Performance gains by algorithmic
disambiguation are pronounced when many forenames are initialized or homonym is
prevalent. As the ratios of full forenames increase, however, they become
marginal compared to the performances by string matching. Using a small portion
of forename strings does not reduce much the performances of both heuristic and
algorithmic disambiguation compared to using full-length strings. These
findings provide practical suggestions such as restoring initialized forenames
into a full-string format via record linkage for improved disambiguation
performances.
