One Tag to bind them all: Measuring Term Abstractness in Social Metadata
author: Dominik Benz, Department of Electrical Engineering and Computer Sciences, University of Kassel
published: July 7, 2011, recorded: June 2011, views: 4034
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial stepstones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, for example, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of term abstractness, including Yago, DMOZ, Wordnet and a taxonomy derived from Wikipedia. Then, we introduce and apply several folksonomy-based methods to measure the level of abstractness of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the abstractness of tags in social tagging systems can be approximated with simple measures. We further corroborate this assumption by an examplary user study. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !