Search (4 results, page 1 of 1)

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Abstract

Purpose - The purpose of this paper is to explore the information needs of one user group, public library fiction readers, in order to reveal a design of an online community at the local level. Examination of user-generated metadata can reveal new approaches to information architecture. Design/methodology/approach - A literature review into behaviors of virtual communities; surveying public library readers regarding search behavior characteristics - the survey included a sample "tagging" exercise to determine whether public library communities could create meaningful metadata for retrieval purposes. Findings - The use of relevance as an indicator of tag quality is flawed: in a survey, public library readers "tagged" the novel The Da Vinci Code. The resulting collection of tags provided a richer description of the book than did the social book-related web site www.librarything.com. Tag collections can be broken down into different categories, each reflecting a different "facet" of the novel: character, plot, subject/topic, setting, and genre. Faceted structure to tags enables users to choose the context of the tag to the novel. Research limitations/implications - This research is relevant in the world of social networking sites, online communities, or any other such system where users generate descriptive metadata. Examination of such metadata can reveal facets, which can guide the architect/librarian in the design of a versatile architecture. Originality/value - This research resulted in a manifold design for a public-library-based online community that allowed for the full expression of users' information needs. This research introduces a faceted structure to current approaches for user-generated metadata, adding versatility to search terms.

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Abstract

This paper examines the content description of fictional works, specifically novels. The data for the empirical part of the study was gathered in Finnish public libraries. The aim of the study is to find out how library clients and library professionals of public libraries describe novels by abstracting them - what are differences in their characterisations and what are the similarities between their abstracts. Also, a taxonomy of novel abstracts is given. The abstracts are classified into four categories in the taxonomy: plot or thematic abstracts, cultural-historical abstracts, subjective or personal abstracts and critical abstracts. In the final portion of the paper, a model for a search and retrieval system for fiction is presented.

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Abstract

Displays grouping retrieved bibliographic record sets into categories or clusters may communicate search results more quickly and effectively to users than current catalog displays providing long alphabetical lists of records. Bibliographic records associated with three large fiction works are analyzed to discover the presence of relationship-type indicators. Preliminary results show that 94% of the records in this study contained indicators of cluster type that would allow them to be correctly identified automatically. However, the clusters formed by the relationship types used here are of unequal size. Because of this, it is suggested that alternative strategies be investigated for their potential to create more useful clustered displays.

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Abstract

Displays grouping retrieved bibliographic record sets into categories or clusters may communicate search results more quickly and effectively to users than current catalogs providing long alphabetical lists of records. In this research, automatic clustering based on types of relationships, including translation, presence of illustrations, etc., is proposed as a model for clustering. Bibliographic records associated with three large fiction works (Kidnapped by Robert Lewis Stevenson, Bleak House by Charles Dickens, and Three Musketeers by Alexandre Dumas) are analyzed to discover the presence of relationship-type indicators to determine the extent to which an automatic clustering program would succeed in clustering work records. Preliminary results show that 94 percent of the records in this study contained indicators of cluster type that would allow them to be correctly identified automatically