Abstract

In Web 2.0 services “prosumers “- producers and consumers- collaborate not only for the purpose of creating content, but to index these pieces of information as well. Folksonomies permit actors to describe documents with subject headings, “tags“, without regarding any rules. Apart from a lot of benefits folksonomies have many shortcomings (e.g., lack of precision). In order to solve some of the problems we propose interpreting tags as natural language terms. Accordingly, we can introduce methods of NLP to solve the tags ’ linguistic problems. Additionally we present criteria for tagged documents to create a ranking by relevance (tag distribution, collaboration and actor-based aspects). We would like to open the discussion concerning the following aspects: Which tag distributions seem to be characteristic for folksonomies and how can we use these distributions effectively in information retrieval? What are the problems of indexing by using tags, especially regarding indexing photos and videos? How may we use factors of collaborative indexing for relevance ranking? Information Indexing Using “Collective Intelligence ” in Web 2.0 Services In the early years of the World Wide Web only few experts were able to distribute information via this new medium. The majority of the people dealing with the WWW solely acted as passive users. Yet, since the

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