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  • × author_ss:"Gnoli, C."
  • × type_ss:"a"
  • × type_ss:"el"
  1. Gnoli, C.; Pusterla, L.; Bendiscioli, A.; Recinella, C.: Classification for collections mapping and query expansion (2016) 0.05
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    Theme
    Klassifikationssysteme im Online-Retrieval
    Semantisches Umfeld in Indexierung u. Retrieval
  2. Almeida, P. de; Gnoli, C.: Fiction in a phenomenon-based classification (2021) 0.00
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    Abstract
    In traditional classification, fictional works are indexed only by their form, genre, and language, while their subject content is believed to be irrelevant. However, recent research suggests that this may not be the best approach. We tested indexing of a small sample of selected fictional works by Integrative Levels Classification (ILC2), a freely faceted system based on phenomena instead of disciplines and considered the structure of the resulting classmarks. Issues in the process of subject analysis, such as selection of relevant vs. non-relevant themes and citation order of relevant ones, are identified and discussed. Some phenomena that are covered in scholarly literature can also be identified as relevant themes in fictional literature and expressed in classmarks. This can allow for hybrid search and retrieval systems covering both fiction and nonfiction, which will result in better leveraging of the knowledge contained in fictional works.