Evaluating Similarity Measures for Emergent Semantics of Social Tagging

author: Benjamin Markines, School of Informatics and Computing, Indiana University
author: Ciro Cattuto, Institute for Scientific Interchange Foundation
author: Filippo Menczer, School of Informatics and Computing, Indiana University
author: Dominik Benz, Department of Electrical Engineering and Computer Sciences, University of Kassel
author: Andreas Hotho, Department of Electrical Engineering and Computer Sciences, University of Kassel
author: Gerd Stumme, Department of Electrical Engineering and Computer Sciences, University of Kassel
published: May 20, 2009,   recorded: April 2009,   views: 351
Categories

Slides

Related Open Educational Resources

Related content

Report a problem or upload files

If 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.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

Social bookmarking systems are becoming increasingly important data sources for bootstrapping and maintaining Semantic Web applications. Their emergent information structures have become known as folksonomies. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, and which measures are best suited for applications such as community detection, navigation support, semantic search, user profiling and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures, which are derived from several established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity between tags and between resources and consider different methods to aggregate annotations across users. After comparing the ability of several tag similarity measures to predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory Project. We also investigate the issue of scalability. We find that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity.

See Also:

Download slides icon Download slides: www09_markines_esst_01.pdf (3.5┬áMB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: