Abstract
Current research in large-scale information management systems is focused on unsupervised methods and techniques for information processing. Such approaches support scalability in regard to present-day exponential growth in information processing needs. In this paper we focus on the problem of automated quality evaluation of a completely unsupervised metadata extraction process in the Digital Libraries domain. In particular, we investigate resulting metadata quality applying specific extraction methodology for scientific documents. We propose and discuss precise quality metrics and measure the dynamics of such quality metrics as a function of the extracted information from the repository and size of the repository.
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Ivanyukovich, A., Marchese, M., Reuther, P. (2007). Assessing Quality Dynamics in Unsupervised Metadata Extraction for Digital Libraries. In: Kovács, L., Fuhr, N., Meghini, C. (eds) Research and Advanced Technology for Digital Libraries. ECDL 2007. Lecture Notes in Computer Science, vol 4675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74851-9_41
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DOI: https://doi.org/10.1007/978-3-540-74851-9_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74850-2
Online ISBN: 978-3-540-74851-9
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