"by letting users tag (...), we're (building) systems that, like the Web itself, do a better job of letting individuals create value for one another, often without realizing it."
Part of the allure of classifying things by assigning tags to them is that the user can give free reign to sloppiness. There is no authority —human or computational— passing judgment on the appropriateness or validity of tags, because tags have to mak
The growing popularity of social tagging systems promises to alleviate the knowledge bottleneck that slows the full materialization of the Semantic Web, as these systems are cheap, extendable, scalable and respond quickly to user needs. However, for the sake of knowledge workflow, one needs to find a compromise between the ungoverned nature of folksonomies and the controlled vocabulary of domain-experts. In this paper, we address this concern by first devising a method that automatically combines folksonomies with domain-expert ontologies resulting in an enriched folksonomy. We then introduce a new algorithm based on frequent itemsets mining that efficiently learns an ontology over the concepts present in the enriched folksonomy. Moreover, we propose a new benchmark for ontology evaluation, which is used in the context of information finding, since this is one of the leading motivations for using ontologies in social tagging systems, to quantitatively assess our method. We conduct experiments on real data and empirically show the effectiveness of our approach.
F. Limpens, F. Gandon, and M. Buffa. Automated Software Engineering - Workshops, 2008. ASE Workshops 2008. 23rd IEEE/ACM International Conference on, (September 2008)
C. Damme, T. Coenen, and E. Vandijck. Scalable Computing: Practice and Experience - Scientific International Journal for Parallel and Distributed Computing, 9 (4):
293-301(December 2008)
F. Limpens, F. Gandon, and M. Buffa. Automated Software Engineering - Workshops, 2008. ASE Workshops 2008. 23rd IEEE/ACM International Conference on, (September 2008)