Of mice and terms: clustering algorithms on ambiguous terms in folksonomies

摘要

Developed using the principles of the Model-View-Controller architectural pattern, FolksEngine is a parametric search engine for folksonomies that allows us to test arbitrary search improvement algorithms by specifying them in three phases: expansion, where the original query is converted in multiple ones according to semantic rules associated to the query terms, search, executing the queries on a standard folksonomy search engine such as Delicious, and ranking, sorting the results according to rules. In this paper we extend our previous studies using FolksEngine and offer a new query expansion algorithms based on Natural Language Processing techniques, and a new view for the results based on Semantic Web technologies. We also describe some tests of the algorithms developed, in order to obtain a clear and effective evaluation of them.