Computer Science – Artificial Intelligence
Scientific paper
2010-05-26
Computer Science
Artificial Intelligence
6 Pages, To appear at AAAI Workshop on Statistical Relational AI
Scientific paper
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.
Getoor Lise
Lerman Kristina
Plangprasopchok Anon
No associations
LandOfFree
Integrating Structured Metadata with Relational Affinity Propagation does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Integrating Structured Metadata with Relational Affinity Propagation, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Integrating Structured Metadata with Relational Affinity Propagation will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-191712