Constructing folksonomies from user-specified relations on flickr Export

@inproceedings{citeulike:4965544, abstract = {Automatic folksonomy construction from tags has attracted much attention recently. However, inferring hierarchical relations between concepts from tags has a drawback in that it is difficult to distinguish between more popular and more general concepts. Instead of tags we propose to use user-specified relations for learning folksonomy. We explore two statistical frameworks for aggregating many shallow individual hierarchies, expressed through the collection/set relations on the social photosharing site Flickr, into a common deeper folksonomy that reflects how a community organizes knowledge. Our approach addresses a number of challenges that arise while aggregating information from diverse users, namely noisy vocabulary, and variations in the granularity level of the concepts expressed. Our second contribution is a method for automatically evaluating learned folksonomy by comparing it to a reference taxonomy, e.g., the Web directory created by the Open Directory Project. Our empirical results suggest that user-specified relations are a good source of evidence for learning folksonomies.}, address = {New York, NY, USA}, author = {Plangprasopchok, Anon and Lerman, Kristina}, booktitle = {WWW '09: Proceedings of the 18th international conference on World wide web}, citeulike-article-id = {4965544}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1526814}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1526709.1526814}, doi = {10.1145/1526709.1526814}, isbn = {978-1-60558-487-4}, keywords = {2009, social\_annotation, taxonomy, www}, location = {Madrid, Spain}, pages = {781--790}, posted-at = {2009-08-29 12:57:13}, priority = {2}, publisher = {ACM}, title = {Constructing folksonomies from user-specified relations on flickr}, url = {http://dx.doi.org/10.1145/1526709.1526814}, year = {2009} }

TY - CONF ID - citeulike:4965544 L3 - citeulike-article-id:4965544 N2 - Automatic folksonomy construction from tags has attracted much attention recently. However, inferring hierarchical relations between concepts from tags has a drawback in that it is difficult to distinguish between more popular and more general concepts. Instead of tags we propose to use user-specified relations for learning folksonomy. We explore two statistical frameworks for aggregating many shallow individual hierarchies, expressed through the collection/set relations on the social photosharing site Flickr, into a common deeper folksonomy that reflects how a community organizes knowledge. Our approach addresses a number of challenges that arise while aggregating information from diverse users, namely noisy vocabulary, and variations in the granularity level of the concepts expressed. Our second contribution is a method for automatically evaluating learned folksonomy by comparing it to a reference taxonomy, e.g., the Web directory created by the Open Directory Project. Our empirical results suggest that user-specified relations are a good source of evidence for learning folksonomies. CY - Madrid, Spain SN - 978-1-60558-487-4 AD - New York, NY, USA EP - 790 TI - Constructing folksonomies from user-specified relations on flickr PB - ACM T2 - WWW '09: Proceedings of the 18th international conference on World wide web SP - 781 KW - 2009 KW - social_annotation KW - taxonomy KW - www AU - Plangprasopchok, Anon AU - Lerman, Kristina PY - 2009/// UR - http://dx.doi.org/10.1145/1526709.1526814 ER -