Search (2 results, page 1 of 1)

  • × author_ss:"Maltese, V."
  • × year_i:[2010 TO 2020}
  1. Maltese, V.: Digital transformation challenges for universities : ensuring information consistency across digital services (2018) 0.02 
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    Abstract
    Universities struggle to offer complete, up-to-date and consistent information about their key assets to their numerous users across various digital services and communication channels. Key assets include people, papers, books, dissertations, patents, courses, and research projects. The main difficulty stands in the intrinsic data fragmentation and data diversity: data about the key assets is scattered across multiple information silos, data is often duplicated and difficult to correlate due to the diversity in the format, metadata, conventions, and terminology used. We illustrate how this difficulty can be tackled and describe the work carried out at the University of Trento in Italy.
  2. Giunchiglia, F.; Dutta, B.; Maltese, V.: From knowledge organization to knowledge representation (2014) 0.01 
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    Abstract
    So far, within the library and information science (LIS) community, knowledge organization (KO) has developed its own very successful solutions to document search, allowing for the classification, indexing and search of millions of books. However, current KO solutions are limited in expressivity as they only support queries by document properties, e.g., by title, author and subject. In parallel, within the artificial intelligence and semantic web communities, knowledge representation (KR) has developed very powerful end expressive techniques, which via the use of ontologies support queries by any entity property (e.g., the properties of the entities described in a document). However, KR has not scaled yet to the level of KO, mainly because of the lack of a precise and scalable entity specification methodology. In this paper we present DERA, a new methodology inspired by the faceted approach, as introduced in KO, that retains all the advantages of KR and compensates for the limitations of KO. DERA guarantees at the same time quality, extensibility, scalability and effectiveness in search.

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