چكيده به لاتين
Abstract:
With the everyday growth of tagging systems and increase in users' interest in sharing digital data, users assign a set of tags to their contents of interest and share them with other users.
On the other hand, since there is no surveillance and there is a lack of an accurate structure in such systems, these tags face a lot of problems including human errors, existence of different meanings for a tag and synonyms and more important of all, high volume, and cannot be used in search and filtering operations accurately.
In this paper, we propose a new approach along with a set of algorithms for eliminating redundancies and reducing the data volume available in the tagging systems. In addition we use the Wordnet database to enrich the semantic relevance of tags in order to extract information associated with these systems.
Keywords: tagging algorithms, social recommender systems, Folksonomy, semantic web.
