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.

The Open Directory Project is clearly one of the largest collaborative efforts to manually annotate web pages. This effort involves over 65,000 editors and resulted in metadata specifying topic and importance for more than 4 million web pages. Still, given that this number is just about 0.05 percent of the Web pages indexed by Google, is this effort enough to make a difference? In this paper we discuss how these metadata can be exploited to achieve high quality personalized web search. First, we address this by introducing an additional criterion for web page ranking, namely the distance between a user profile defined using ODP topics and the sets of ODP topics covered by each URL returned in regular web search. We empirically show that this enhancement yields better results than current web search using Google. Then, in the second part of the paper, we investigate the boundaries of biasing PageRank on subtopics of the ODP in order to automatically extend these metadata to the whole web.

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Abstract. Existing desktop search applications, trying to keep up with the rapidly increasing storage capacities of our hard disks, offer an incomplete solution for information retrieval. In this paper we describe our Beagle ++ desktop search prototype, which enhances conventional fulltext search with semantics and ranking modules. This prototype extracts and stores activity-based metadata explicitly as RDF annotations. Our main contributions are extensions we integrate into the Beagle desktop search infrastructure to exploit this additional contextual information for searching and ranking the resources on the desktop. Contextual information plus ranking brings desktop search much closer to the performance of web search engines. Initially disconnected sets of resources on the desktop are connected by our contextual metadata, PageRank derived algorithms allow us to rank these resources appropriately. First experiments investigating precision and recall quality of our search prototype show encouraging improvements over standard search.

Abstract. With increasing storage capacities on current PCs, searching the World Wide Web has ironically become more efficient than searching one's own personal computer. The recently introduced desktop search engines are a first step towards coping with this problem, but not yet a satisfying solution. The reason for that is that desktop search is actually quite different from its web counterpart. Documents on the desktop are not linked to each other in a way comparable to the web, which means that result ranking is poor or even inexistent, because algorithms like PageRank cannot be used for desktop search. On the other hand, desktop search could potentially profit from a lot of implicit and explicit semantic information available in emails, folder hierarchies, browser cache contexts and others. This paper investigates how to extract and store these activity based context information explicitly as RDF metadata and how to use them, as well as additional background information and ontologies, to enhance desktop search.