Hierarchical Classification of OAI Metadata Using the DDC Taxonomy

Abstract

In the area of digital library services, the access to subject-specific metadata of scholarly publications is of utmost interest. One of the most prevalent approaches for metadata exchange is the XML-based Open Archive Initiative (OAI) Protocol for Metadata Harvesting (OAI-PMH). However, due to its loose requirements regarding metadata content there is no strict standard for consistent subject indexing specified, which is furthermore needed in the digital library domain. This contribution addresses the problem of automatic enhancement of OAI metadata by means of the most widely used universal classification schemes in libraries—the Dewey Decimal Classification (DDC). To be more specific, we automatically classify scientific documents according to the DDC taxonomy within three levels using a machine learning-based classifier that relies solely on OAI metadata records as the document representation. The results show an asymmetric distribution of documents across the hierarchical structure of the DDC taxonomy and issues of data sparseness. However, the performance of the classifier shows promising results on all three levels of the DDC.