Supporting Taxonomy Development and Evolution by Means of Crowdsourcing

Binh Vu, Matthias Hemmje

Abstract

Information overload continues to be a challenge. By dividing the material into many different small subsets, classification based on a taxonomy makes data exploration and retrieval faster and more accurate. Instead of having to know the exact keywords that describe the knowledge resource, users can browse and search for them by selecting the categories that the resource is most likely to belong. Nevertheless, developing taxonomies is not an easy task. It requires the authors to have a certain amount of knowledge in the domain. Furthermore, the workload will increase as any new taxonomy needs to be frequently updated to remain relevant and useful. To combat these problems, this paper proposes another approach to crowdsource taxonomy development and evolution. We describe in this paper the concept of this approach along with different types of evaluations targeting on the one hand to demonstrate the feasibility of the approach and the usability of the initial prototype as well as on the other hand the quality and effectiveness of the chosen method.

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