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Striving for an Adequate Vocabulary: Next Generation ‘Metadata’

  • Dieter Fellner
  • Sven Havemann
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Digital Libraries (DLs) in general and technical or cultural preservation applications in particular offer a rich set of multimedia objects like audio, music, images, videos, and 3D models. But instead of handling these objects consistently as regular documents — in the same way we handle text documents — most applications handle them differently. This is due to the fact that ‘standard’ tasks like content categorization, indexing, content representation or summarization have not yet been developed to a stage where DL technology could readily apply it for these types of documents. Instead, these tasks have to be done manually making the activity almost prohibitively expensive. Consequently, the most pressing research challenge is the development of an adequate ‘vocabulary’ to characterize the content and structure of non-textual documents as the key to indexing, categorization, dissemination and access.

We argue that textual metadata items are insufficient for describing images, videos, 3D models, or audio adequately. A new type of generalized vocabulary is needed that permits to express semantic information — which is a prerequisite for a retrieval of generalized documents based on their content, rather than on static textual annotations. The crucial question being which methods and which types of technology will best support the definition of vocabularies and ontologies for non-textual documents.

We present one such method for the domain of 3D models. Our approach allows to differentiate between the structure and the appearance of a 3D model, and we believe that this formalism can be generalized to other types of media.

Keywords

Digital Library Semantic Information Generalize Document Multimedia Object Shape Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin · Heidelberg 2006

Authors and Affiliations

  • Dieter Fellner
    • 1
  • Sven Havemann
    • 1
  1. 1.Institut für ComputerGraphikTU BraunschweigBraunschweigGermany

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