Schema Evolution publication categorizer

In this work we present GOAL (Genetics for Ontology Alignments)
a new approach to compute the optimal ontology alignment function
for a given ontology input set. Although this problem could be solved
by an exhaustive search when the number of similarity measures is low,
our method is expected to scale better for a high number of measures.
Our approach is a genetic algorithm which is able to work with several
goals: maximizing the alignment precision, maximizing the alignment recall,
maximizing the f-measure or reducing the number of false positives.
Moreover, we test it here by combining some cutting-edge similarity measures
over a standard benchmark, and the results obtained show several
advantages in relation to other techniques.