  National exercises for the evaluation of research activity by universities
are becoming regular practice in ever more countries. These exercises have
mainly been conducted through the application of peer-review methods.
Bibliometrics has not been able to offer a valid large-scale alternative
because of almost overwhelming difficulties in identifying the true author of
each publication. We will address this problem by presenting a heuristic
approach to author name disambiguation in bibliometric datasets for large-scale
research assessments. The application proposed concerns the Italian university
system, consisting of 80 universities and a research staff of over 60,000
scientists. The key advantage of the proposed approach is the ease of
implementation. The algorithms are of practical application and have
considerably better scalability and expandability properties than
state-of-the-art unsupervised approaches. Moreover, the performance in terms of
precision and recall, which can be further improved, seems thoroughly adequate
for the typical needs of large-scale bibliometric research assessments.
