Abstract
Effective daily processing of large amounts of paper documents in office environments requires the application of semantic-based indexing techniques during the transformation of paper documents to electronic format. For this purpose a combination of both XML and knowledge technologies can be used. XML distinguishes between data, its structure and semantics, allowing the exchange of data elements that carry descriptions of their meaning, usage and relationship. Moreover, the combination with XSLT enables any browser to render the original layout structure of the paper documents accurately. However, an effective transformation of paper documents into XML format is a complex process involving several steps. In this paper we propose the application of knowledge technologies to many document processing steps, namely rule-based systems for semantic indexing of documents and the extraction of the necessary knowledge by means of machine learning techniques. This approach has been implemented in the system Wisdom++, which is currently used in the European project COLLATE (Collaboratory for Annotation, Indexing and Retrieval of Digitized Historical Archive Material) to provide film archivists with a tool for the automated annotation of historical documents in film archives.
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Malerba, D., Ceci, M., Berardi, M. (2003). XML and Knowledge Technologies for Semantic-Based Indexing of Paper Documents. In: Mařík, V., Retschitzegger, W., Štěpánková, O. (eds) Database and Expert Systems Applications. DEXA 2003. Lecture Notes in Computer Science, vol 2736. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45227-0_26
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DOI: https://doi.org/10.1007/978-3-540-45227-0_26
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