Search (4 results, page 1 of 1)

  • × theme_ss:"Visualisierung"
  • × theme_ss:"Klassifikationssysteme im Online-Retrieval"
  1. Chowdhury, S.; Chowdhury, G.G.: Using DDC to create a visual knowledge map as an aid to online information retrieval (2004) 0.08
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    Abstract
    Selection of search terms in an online search environment can be facilitated by the visual display of a knowledge map showing the various concepts and their links. This paper reports an a preliminary research aimed at designing a prototype knowledge map using DDC and its visual display. The prototype knowledge map created using the Protégé and TGViz freeware has been demonstrated, and further areas of research in this field are discussed.
    Content
    1. Introduction Web search engines and digital libraries usually expect the users to use search terms that most accurately represent their information needs. Finding the most appropriate search terms to represent an information need is an age old problem in information retrieval. Keyword or phrase search may produce good search results as long as the search terms or phrase(s) match those used by the authors and have been chosen for indexing by the concerned information retrieval system. Since this does not always happen, a large number of false drops are produced by information retrieval systems. The retrieval results become worse in very large systems that deal with millions of records, such as the Web search engines and digital libraries. Vocabulary control tools are used to improve the performance of text retrieval systems. Thesauri, the most common type of vocabulary control tool used in information retrieval, appeared in the late fifties, designed for use with the emerging post-coordinate indexing systems of that time. They are used to exert terminology control in indexing, and to aid in searching by allowing the searcher to select appropriate search terms. A large volume of literature exists describing the design features, and experiments with the use, of thesauri in various types of information retrieval systems (see for example, Furnas et.al., 1987; Bates, 1986, 1998; Milstead, 1997, and Shiri et al., 2002).
  2. Vizine-Goetz, D.: DeweyBrowser (2006) 0.02
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    Abstract
    The DeweyBrowser allows users to search and browse collections of library resources organized by the Dewey Decimal Classification (DDC) system. The visual interface provides access to several million records from the OCLC WorldCat database and to a collection of records derived from the abridged edition of DDC. The prototype was developed out of a desire to make the most of Dewey numbers assigned to library materials and to explore new ways of providing access to the DDC.
  3. Slavic, A.: Interface to classification : some objectives and options (2006) 0.01
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    Abstract
    This is a preprint to be published in the Extensions & Corrections to the UDC. The paper explains the basic functions of browsing and searching that need to be supported in relation to analytico-synthetic classifications such as Universal Decimal Classification (UDC), irrespective of any specific, real-life implementation. UDC is an example of a semi-faceted system that can be used, for instance, for both post-coordinate searching and hierarchical/facet browsing. The advantages of using a classification for IR, however, depend on the strength of the GUI, which should provide a user-friendly interface to classification browsing and searching. The power of this interface is in supporting visualisation that will 'convert' what is potentially a user-unfriendly indexing language based on symbols, to a subject presentation that is easy to understand, search and navigate. A summary of the basic functions of searching and browsing a classification that may be provided on a user-friendly interface is given and examples of classification browsing interfaces are provided.
  4. Beagle, D.: Visualizing keyword distribution across multidisciplinary c-space (2003) 0.01
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    Abstract
    But what happens to this awareness in a digital library? Can discursive formations be represented in cyberspace, perhaps through diagrams in a visualization interface? And would such a schema be helpful to a digital library user? To approach this question, it is worth taking a moment to reconsider what Radford is looking at. First, he looks at titles to see how the books cluster. To illustrate, I scanned one hundred books on the shelves of a college library under subclass HT 101-395, defined by the LCC subclass caption as Urban groups. The City. Urban sociology. Of the first 100 titles in this sequence, fifty included the word "urban" or variants (e.g. "urbanization"). Another thirty-five used the word "city" or variants. These keywords appear to mark their titles as the heart of this discursive formation. The scattering of titles not using "urban" or "city" used related terms such as "town," "community," or in one case "skyscrapers." So we immediately see some empirical correlation between keywords and classification. But we also see a problem with the commonly used search technique of title-keyword. A student interested in urban studies will want to know about this entire subclass, and may wish to browse every title available therein. A title-keyword search on "urban" will retrieve only half of the titles, while a search on "city" will retrieve just over a third. There will be no overlap, since no titles in this sample contain both words. The only place where both words appear in a common string is in the LCC subclass caption, but captions are not typically indexed in library Online Public Access Catalogs (OPACs). In a traditional library, this problem is mitigated when the student goes to the shelf looking for any one of the books and suddenly discovers a much wider selection than the keyword search had led him to expect. But in a digital library, the issue of non-retrieval can be more problematic, as studies have indicated. Micco and Popp reported that, in a study funded partly by the U.S. Department of Education, 65 of 73 unskilled users searching for material on U.S./Soviet foreign relations found some material but never realized they had missed a large percentage of what was in the database.