document: Pragmatic evaluation of folksonomies Recently , a number of algorithms have been proposed to obtain hierarchical structures - so-called folksonomies - from social tagging data . Work on these algorithms is in part driven by a belief that folksonomies are useful for tasks such as : ( a ) Navigating social tagging systems and ( b ) Acquiring semantic relationships between tags . While the promises and pitfalls of the latter have been studied to some extent , we know very little about the extent to which folksonomies are pragmatically useful for navigating social tagging systems . This paper sets out to address this gap by presenting and applying a pragmatic framework for evaluating folksonomies . We model exploratory navigation of a tagging system as decentralized search on a network of tags . Evaluation is based on the fact that the performance of a decentralized search algorithm depends on the quality of the background knowledge used . The key idea of our approach is to use hierarchical structures learned by folksonomy algorithm as background knowledge for decentralized search . Utilizing decentralized search on tag networks in combination with different folksonomies as hierarchical background knowledge allows us to evaluate navigational tasks in social tagging systems . Our experiments with four state-of-the-art folksonomy algorithms on five different social tagging datasets reveal that existing folksonomy algorithms exhibit significant , previously undiscovered , differences with regard to their utility for navigation . Our results are relevant for engineers aiming to improve navigability of social tagging systems and for scientists aiming to evaluate different folksonomy algorithms from a pragmatic perspective .
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