A probabilistic approach for learning folksonomies from structured data

Abstract

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 [7] 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 realworld 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.

Hierarchical networks of scientific journals

“…A very closely related problem is that given by the automatized categorization of free tags appearing in various online content (Heymann and Garcia-Molina, 2006;Schmitz, 2006;Damme et al, 2007;Plangprasopchok et al, 2011;Tibély et al, 2013;Velardi et al, 2013). In recent years, the voluntary tagging of photos, films, books and so on, with free words has become popular on the Internet in blogs, various file-sharing platforms, online stores and news portals.…”

Exposing, formalizing and reasoning over the latent semantics of tags in multimodal data sources

“…Plangprasopchok and Lerman (2009) introduced statistical frameworks that use user-specified relations and aggregate individual hierarchies. Later they (Plangprasopchok et al, 2011) described an unsupervised probabilistic approach that integrated smaller, noisy structures into a few complex structures. Plangprasopchok et al (2010) learned folksonomies from social metadata on Flickr by using relational clustering.…”

Leveraging User Diversity to Harvest Knowledge on the Social Web

“…Following Plangprasopchok et al [13] we call a directory a user creates to organize photos on Flickr a sapling. 1 The root node of the sapling corresponds to a user's collection, and inherits its name, while the leaves correspond to the collection's constituent sets (or other collections) and inherit their names. The photos the user assigns to a set are tagged, and we propagate these tags to sets and to their parent collections.…”

“…We call the latter users experts and the former novice. By manually inspecting saplings created by Flickr users, we found that structure and semantic consistency are two important factors distinguishing expert from novice 1 Saplings are not always tree-like. In these cases we convert them to trees.…”

“…Recently, Plangprasopchok et al [1] proposed a method to learn folksonomies by integrating structured annotations from many users, specifically, personal directories created by individual Flickr users to organize their photos. The method extends affinity propagation [2] to use structural information to concurrently combine many shallow personal directories into a larger common taxonomy.…”