Search (3 results, page 1 of 1)

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Abstract

While support for subject searching has been traditionally advocated for in library catalogs, often in the form of a catalog objective to find everything that a library has on a certain topic, research has shown that subject access has not been satisfactory. Many existing online catalogs and discovery services do not seem to make good use of the intellectual effort invested into assigning controlled subject index terms and classes. For example, few support hierarchical browsing of classification schemes and other controlled vocabularies with hierarchical structures, few provide end-user-friendly options to choose a more specific concept to increase precision, a broader concept or related concepts to increase recall, to disambiguate homonyms, or to find which term is best used to name a concept. Optimum subject access in library catalogs and discovery services is analyzed from the perspective of earlier research as well as contemporary conceptual models and cataloguing codes. Eighteen proposed features of what this should entail in practice are drawn. In an exploratory qualitative study, the three most common discovery services used in Swedish academic libraries are analyzed against these features. In line with previous research, subject access in contemporary interfaces is demonstrated to less than optimal. This is in spite of the fact that individual collections have been indexed with controlled vocabularies and a significant number of controlled vocabularies have been mapped to each other and are available in interoperable standards. Strategic action is proposed to build research-informed (inter)national standards and guidelines.

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Abstract

Automatic subject indexing addresses problems of scale and sustainability and can be at the same time used to enrich existing metadata records, establish more connections across and between resources from various metadata and resource collec-tions, and enhance consistency of the metadata. In this work, au-tomatic subject indexing focuses on assigning index terms or classes from established knowledge organization systems (KOSs) for subject indexing like thesauri, subject headings systems and classification systems. The following major approaches are dis-cussed, in terms of their similarities and differences, advantages and disadvantages for automatic assigned indexing from KOSs: "text categorization," "document clustering," and "document classification." Text categorization is perhaps the most wide-spread, machine-learning approach with what seems generally good reported performance. Document clustering automatically both creates groups of related documents and extracts names of subjects depicting the group at hand. Document classification re-uses the intellectual effort invested into creating a KOS for sub-ject indexing and even simple string-matching algorithms have been reported to achieve good results, because one concept can be described using a number of different terms, including equiv-alent, related, narrower and broader terms. Finally, applicability of automatic subject indexing to operative information systems and challenges of evaluation are outlined, suggesting the need for more research.

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Date

22. 8.2014 17:12:54