  Learning structured representations has emerged as an important problem in
many domains, including document and Web data mining, bioinformatics, and image
analysis. One approach to learning complex structures is to integrate many
smaller, incomplete and noisy structure fragments. In this work, we present an
unsupervised probabilistic approach that extends affinity propagation to
combine the small ontological fragments into a collection of integrated,
consistent, and larger folksonomies. This is a challenging task because the
method must aggregate similar structures while avoiding structural
inconsistencies and handling noise. We validate the approach on a real-world
social media dataset, comprised of shallow personal hierarchies specified by
many individual users, collected from the photosharing website Flickr. Our
empirical results show that our proposed approach is able to construct deeper
and denser structures, compared to an approach using only the standard affinity
propagation algorithm. Additionally, the approach yields better overall
integration quality than a state-of-the-art approach based on incremental
relational clustering.
