RATC: A Robust Automated Tag Clustering Technique

Abstract

Nowadays, the most dominant and noteworthy web information sources are developed according to the collaborative-web paradigm, also known as Web 2.0. In particular, it represents a novel paradigm in the way users interact with the web. Users (also called prosumers) are no longer passive consumers of published content, but become involved, implicitly and explicitly, as they cooperate by providing their own resources in an “architecture of participation”. In this scenario, collaborative tagging, i.e., the process of classifying shared resources by using keywords, becomes more and more popular. The main problem in such task is related to well-known linguistic phenomena, such as polysemy and synonymy, making effective content retrieval harder. In this paper, an approach that monitors users activity in a tagging system and dynamically quantifies associations among tags is presented. The associations are then used to create tags clusters. Experiments are performed comparing the proposed approach with a state-of-the-art tag clustering technique. Results –given in terms of classical precision and recall– show that the approach is quite effective in the presence of strongly related tags in a cluster.