TY  - CONF
AU  - Plangprasopchok, Anon
AU  - Lerman, Kristina
AU  - Getoor, Lise
A2  - 
T1  - A Probabilistic Approach for Learning Folksonomies from Structured Data
T2  - Proceedings of the 4th ACM Web Search and Data Mining Conference
PB  - 
C1  - 
PY  - 2010/
CY  -  
VL  - 
IS  - 
SP  - 
EP  - 
UR  - http://arxiv.org/abs/1011.3557
DO  - 
KW  - affinity_propagation
KW  - deletethistag
KW  - folksonomy
KW  - learning
KW  - ontology
L1  - 
SN  - 
N1  - A Probabilistic Approach for Learning Folksonomies from Structured Data
N1  - 
AB  - 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. 
ER  -