A Probabilistic Approach for Learning Folksonomies from Structured Data

TL;DR

This paper introduces an unsupervised probabilistic method that extends affinity propagation to merge noisy, incomplete social media hierarchies into larger, consistent folksonomies, improving structure depth and integration quality.

Contribution

It presents a novel probabilistic extension of affinity propagation for integrating small, noisy ontological fragments into larger folksonomies, validated on real-world social media data.

Findings

Constructs deeper, denser folksonomies than standard affinity propagation.

Achieves better integration quality than incremental relational clustering.

Successfully handles noise and structural inconsistencies in social media 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 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.