Folksonomy-Based Collabulary Learning

Abstract

Abstract. The growing popularity of social tagging systems promises to allevi- ate the knowledge bottleneck that slows down the full materialization of the Se- manticWeb since these systems allow ordinary users to create and share knowl- edge in a simple, cheap, and scalable representation, usually known as folkson- omy. However, for the sake of knowledge workflow, one needs to find a com- promise between the uncontrolled nature of folksonomies and the controlled and more systematic vocabulary of domain experts. In this paper we propose to ad- dress this concern by devising a method that automatically enriches a folkson- omy with domain expert knowledge and by introducing a novel algorithm based on frequent itemset mining techniques to efficiently learn an ontology over the enriched folksonomy. In order to quantitatively assess our method, we propose a new benchmark for task-based ontology evaluation where the quality of the on- tologies is measured based on how helpful they are for the task of personalized information finding. We conduct experiments on real data and empirically show the effectiveness of our approach.