Integrating Structured Metadata with Relational Affinity Propagation
  Structured and semi-structured data describing entities, taxonomies and
ontologies appears in many domains. There is a huge interest in integrating
structured information from multiple sources; however integrating structured
data to infer complex common structures is a difficult task because the
integration must aggregate similar structures while avoiding structural
inconsistencies that may appear when the data is combined. In this work, we
study the integration of structured social metadata: shallow personal
hierarchies specified by many individual users on the SocialWeb, and focus on
inferring a collection of integrated, consistent taxonomies. We frame this task
as an optimization problem with structural constraints. We propose a new
inference algorithm, which we refer to as Relational Affinity Propagation (RAP)
that extends affinity propagation (Frey and Dueck 2007) by introducing
structural constraints. We validate the approach on a real-world social media
dataset, 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.
