This chapter traces the history of digital libraries (DLs) in the United States through the funding sources that have supported DL research and development over the past decade and a half. A set of related questions are addressed: How have the mission and goals of funding agencies affected the types of projects that have been funded? What have been the deliverables from funded projects and how have the goals of the funding agencies shaped those deliverables? Funding agencies have exerted strong influence over research and development in DLs, and different funding agencies have funded different types of projects, with varying sets of concerns for driving the various fields that feed into DLs. This paper will address the impact that DL funding has had on the development of research in the field of Library and Information Science, as well as on the practice of librarianship.

Some documents provoke emotions in people viewing them. Will it be possible to describe emotions consistently and use this information in retrieval systems? We tested collective (statistically aggregated) emotion indexing using images as examples. Considering psychological results, basic emotions are anger, disgust, fear, happiness, and sadness. This study follows an approach developed by Lee and Neal (2007) for music emotion retrieval and applies scroll bars for tagging basic emotions and their intensities. A sample comprising 763 persons tagged emotions caused by images (retrieved from www.Flickr.com) applying scroll bars and (linguistic) tags. Using SPSS, we performed descriptive statistics and correlation analysis. For more than half of the images, the test persons have clear emotion favorites. There are prototypical images for given emotions. The document-specific consistency of tagging using a scroll bar is, for some images, very high. Most of the (most commonly used) linguistic tags are on the basic level (in the sense of Rosch’s basic level theory). The distributions of the linguistic tags in our examples follow an inverse power-law. Hence, it seems possible to apply collective image emotion tagging to image information systems and to present a new search option for basic emotions. This article is one of the first steps in the research area of emotional information retrieval (EmIR).

The current issue and full text archive of this journal is available at www.emeraldinsight.com/0737-8831.htm “Power tags ” in information retrieval

Abstract—Collaborative (or social tagging) options are being added to many database catalogs on the assumption that not only those who assign tags but also those who use the catalog find such tags beneficial. But no quantitative analyses of collaborative tags exist to support this assumption. Based on questionnaires mixing collaborative tag clouds from

Purpose – Many Web 2.0 services (including Library 2.0 catalogues) make use of folksonomies. Our essential idea is to cut off all tags in the long tail of a document-specific tag distribution. The remaining tags at the beginning of a tag distribution are considered Power Tags and form a new, additional search option in information retrieval systems. Design/methodology/approach – In a theoretical approach the article discusses document-specific tag distributions (power law and inverse-logistic shape), the development of such distributions (Yule-Simon process and shuffling theory) and introduces search tags (besides the well-known index tags) as a possibility for generating tag distributions. Findings – Search tags are compatible with broad and narrow folksonomies and with all knowledge organization systems (e.g., classification systems and thesauri), while index tags are only applicable in broad folksonomies. Based upon these findings, we present a sketch of an algorithm for mining and processing Power Tags in information retrieval systems. Research limitations – This conceptual approach is in need of empirical evaluation in a concrete retrieval system.