![]() The proposed tool applies ideas from case-based reasoning (CBR) to assist catalogers, supplementing traditional cataloging tools by identifying appropriate subject areas and keywords for incoming material. Some of these data, such as the title, author, advisor, abstract, and date are explicitly given in the digital resource itself, while other data, for instance, subject areas and keywords, typically need to be inferred by the cataloger. For example, a thesis is associated with an author, an advisor, a title, an abstract, one or more subject areas, a small set of keywords (words or short phrases that are used to describe the topic of a resource), and a date of publication. The task of cataloging involves associating a set of metadata with incoming resources. The SciELO Suggester system is an innovative tool developed to facilitate the process of cataloging resources arriving at a library. A variety of solutions based on information technologies have been proposed to assist in the cataloging process (Buckland, 1992, Levy and Marshall, 1995, Park and Lu, 2009, Sølvberg, 2001). Organizing resources associated with diverse topics is a difficult and costly task for the cataloger, who is typically unfamiliar with incoming resources due to their heterogeneous nature. Finally, a specially designed cognitive walk was completed with catalogers, providing additional insights into the strengths and weaknesses of the tool.Īlthough many standardized resources and well-established practices are commonly used to generate library records, the process of cataloging remains a bottleneck in library management. In addition, a heuristic evaluation of the tool was carried out by taking as a starting point the Sirius heuristic-based framework, resulting in a very good score. These evaluations indicate that the use of case-based reasoning provides a powerful alternative to traditional ways of identifying subject areas and keywords in library resources. In both experiments the system has shown very good performance. The tool has been evaluated through a human-subject study with catalogers and through an automatic test using a collection consisting of 5742 training examples and 120 test cases from 12 different subject areas. The system is implemented as a web service and it can be easily used by installing an add-on for the Mozilla Firefox browser. The suggester tool applies case-based reasoning to generate suggestions taken from material previously cataloged in the SciELO scientific electronic library. ![]() Thus, it assists catalogers with their task, as they are typically unfamiliar with the heterogeneous nature of the incoming material. The proposed tool provides useful suggestions about what information to include in newly created records. The SciELO Suggester system is an innovative tool developed to overcome certain general limitations encountered in current mechanisms for entering descriptions of library records. However, the cataloger still has to deal with the difficult task of deciding what information to include. Existing cataloging interfaces are designed to reduce the bottleneck of creating, editing, and refining bibliographic records by offering a convenient framework for data entry.
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