Collaborative tagging has become an increasingly popular means for sharing and organizing Web resources, leading to a huge amount of user generated metadata. These tags represent quite a few different aspects of the resources they describe and it is not obvious whether and how these tags or subsets of them can be used for search. This paper is the first to present an in-depth study of tagging behavior for very different kinds of resources and systemsWeb pages (Del.icio.us), music (Last.fm), and images (Flickr) -and compares the results with anchor text characteristics. We analyze and classify sample tags from these systems, to get an insight into what kinds of tags are used for different resources, and provide statistics on tag distributions in all three tagging environments. Since even relevant tags may not add new information to the search procedure, we also check overlap of tags with content, with metadata assigned by experts and from other sources. We discuss the potential of different kinds of tags for improving search, comparing them with user queries posted to search engines as well as through a user survey. The results are promising and provide more insight into both the use of different kinds of tags for improving search and possible extensions of tagging systems to support the creation of potentially search-relevant tags.

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Entity Retrieval (ER)-in comparison to classical search-aims at finding individual entities instead of relevant documents. Finding a list of entities requires therefore techniques different to classical search engines. In this paper, we present a model to describe entities more formally and how an ER system can be build on top of it. We compare different approaches designed for finding entities in Wikipedia and report on results using standard test collections. An analysis of entity-centric queries reveals different aspects and problems related to ER and shows limitations of current systems performing ER with Wikipedia. It also indicates which approaches are suitable for which kinds of queries.