Bibliographic Classification using the ADS Databases

Alberto Accomazzi; Carolyn S. Grant; Edwin Henneken; Guenther Eichhorn; Markus Demleitner; Michael J. Kurtz; Stephen S. Murray

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Bibliographic Classification using the ADS Databases

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arxiv cs/0511002 v1 pith:CVVQIXV2 submitted 2005-10-31 cs.IR cs.DL

Bibliographic Classification using the ADS Databases

Alberto Accomazzi , Michael J. Kurtz , Guenther Eichhorn , Edwin Henneken , Carolyn S. Grant , Markus Demleitner , Stephen S. Murray This is my paper

classification cs.IR cs.DL

keywords databasesbibliographicrecordsusedbeenclassifydistributionexisting

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We discuss two techniques used to characterize bibliographic records based on their similarity to and relationship with the contents of the NASA Astrophysics Data System (ADS) databases. The first method has been used to classify input text as being relevant to one or more subject areas based on an analysis of the frequency distribution of its individual words. The second method has been used to classify existing records as being relevant to one or more databases based on the distribution of the papers citing them. Both techniques have proven to be valuable tools in assigning new and existing bibliographic records to different disciplines within the ADS databases.

Bibliographic Classification using the ADS Databases

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