Automatic Creation of Mappings between Classification Systems for Bibliographic Data

Magnus Pfeffer

Abstract


Classification systems are an important means to provide
topic based access to large collections. They are utilized by a number of
approaches for faceted browsing, graphical search support and lately also for
collection visualisation and analysis. Most of these approaches have been
developed with a specific classification system in mind and often exploit some
of the inherent characteristics of the system. Collections that are indexed
using local or special classification systems cannot benefit from the vast
majority of innovative applications developed for the more commonly used
classification systems. One way to alleviate this problem is the use of mappings
between classification systems. Traditionally, these mappings have been created
in a manual and time consuming process involving subject specialists.In this
paper, we discuss another approach to automatically create mappings between
classification systems. The approach consists of three steps: First,
bibliographic data from diverse sources that contain items classified by the
required classification systems is aggregated in a single database. Next, a
clustering algorithm is used to group individual issues and editions of the same
work. The basic idea is that for classification purposes, there is no
significant difference across editions and indexing information can thus be
consolidated within the clusters. Finally, the clusters containing information
from both required systems are added up to create a cooccurrence table. This
information can be used to describe correlations between individual classes of
the two classification systems and forms the basis of a full mapping between the
two systems. First results from an application of this approach to data from
German union catalogues and comparing the derived mappings to manually created
ones are quite promising and show the potential of this idea.

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