@conference {14471,
	title = {A probabilistic approach for learning folksonomies from structured data},
	booktitle = {Proceedings of the fourth ACM international conference on Web search and data mining},
	series = {WSDM {\textquoteright}11},
	year = {2011},
	month = {2011///},
	pages = {555 - 564},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	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 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.},
	keywords = {collective knowledge, data mining, folksonomies, social information processing, social metadata, taxonomies},
	isbn = {978-1-4503-0493-1},
	doi = {10.1145/1935826.1935905},
	url = {http://doi.acm.org/10.1145/1935826.1935905},
	author = {Plangprasopchok,Anon and Lerman,Kristina and Getoor, Lise}
}
@conference {14446,
	title = {Growing a tree in the forest: constructing folksonomies by integrating structured metadata},
	booktitle = {Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining},
	series = {KDD {\textquoteright}10},
	year = {2010},
	month = {2010///},
	pages = {949 - 958},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Many social Web sites allow users to annotate the content with descriptive metadata, such as tags, and more recently to organize content hierarchically. These types of structured metadata provide valuable evidence for learning how a community organizes knowledge. For instance, we can aggregate many personal hierarchies into a common taxonomy, also known as a folksonomy, that will aid users in visualizing and browsing social content, and also to help them in organizing their own content. However, learning from social metadata presents several challenges, since it is sparse, shallow, ambiguous, noisy, and inconsistent. We describe an approach to folksonomy learning based on relational clustering, which exploits structured metadata contained in personal hierarchies. Our approach clusters similar hierarchies using their structure and tag statistics, then incrementally weaves them into a deeper, bushier tree. We study folksonomy learning using social metadata extracted from the photo-sharing site Flickr, and demonstrate that the proposed approach addresses the challenges. Moreover, comparing to previous work, the approach produces larger, more accurate folksonomies, and in addition, scales better.},
	keywords = {collective knowledge, data mining, folksonomies, relational clustering, social information processing, social metadata, taxonomies},
	isbn = {978-1-4503-0055-1},
	doi = {10.1145/1835804.1835924},
	url = {http://doi.acm.org/10.1145/1835804.1835924},
	author = {Plangprasopchok,Anon and Lerman,Kristina and Getoor, Lise}
}
