Abstract
We propose a method based on restricted random walk clustering as a (semi-)automated complement for the tedious, error-prone and expensive task of manual indexing in a scientific library. The first stage of our method is to cluster a set of (partially) indexed documents using restricted random walks on usage histories in order to find groups of similar documents. In the second stage, we derive possible keywords for documents without indexing information from the frequencies of keywords assigned to other documents in their respective cluster.
Due to the specific clustering algorithm, the proposed algorithm is still efficient with millions of documents and can be deployed on standard PC hardware.
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Franke, M., Geyer-Schulz, A. (2004). Automated Indexing with Restricted Random Walks on Large Document Sets. In: Heery, R., Lyon, L. (eds) Research and Advanced Technology for Digital Libraries. ECDL 2004. Lecture Notes in Computer Science, vol 3232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30230-8_22
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DOI: https://doi.org/10.1007/978-3-540-30230-8_22
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