Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. 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 navigation support, semantic search, and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures derived from 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 among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory. 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.
Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. 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 navigation support, semantic search, and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures derived from 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 among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory. 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.
J. Hähner, S. Rudolph, S. Tomforde, D. Fisch, B. Sick, N. Kopal, and A. Wacker. 1st International Workshop on „Self-optimisation in organic and autonomic computing systems“ (SAOS13), held in conjunction with 26th International Conference on Architecture of Computing Systems (ARCS 2013), Prague, Czech Republic, (19-22 Februar 2013)
C. Boelmann, T. Weis, M. Engel, and A. Wacker. 18th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2012), Singapore, (17-19 December 2012)
S. Hick, B. Esslinger, and A. Wacker. The 10th International Conference on Education and Information Systems,Technologies and Applications (EISTA 2012), Orlando, Florida, USA, (17-20 Juli 2012)
S. Doerfel, R. Jäschke, A. Hotho, and G. Stumme. Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web, page 9--16. New York, NY, USA, ACM, (2012)
J. Hegenberg, L. Cramar, and L. Schmidt. 10th International IFAC Symposium on Robot Control (Dubrovnik, Croatia 2012), 10, page 793-798. Dubrovnik, International Federation of Automatic Control (IFAC), (2012)
J. Hegenberg, L. Cramar, and L. Schmidt. Datenbrillen - Aktueller Stand von Forschung und Umsetzung sowie zukünftiger Entwicklungsrichtungen (Dortmund 2011), page 29-38. Dortmund, BAuA, (2012)
C. Scholz, M. Atzmueller, and G. Stumme. Proc. Fourth ASE/IEEE International Conference on Social Computing (SocialCom), Boston, MA, USA, IEEE Computer Society, (2012)